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Papers & Technical Reports by Topics

(Papers in Chronicle Order)
   Tony Cai

(Some papers are cross-listed under multiple categories.)

    Statistical Machine Learning
  1. Cai, T. T., Li, X., Long, Q., Su, W., & Wen, G. (2025).
    Optimal detection for language watermarks with pseudorandom collision.
    Technical report.

     

    Transfer Learning

  2. Auddy, A., Cai, T. T., & Chakraborty, A. (2025+).
    Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.
    Journal of the Royal Statistical Society, Series B, to appear.
  3. Cai, T. T. & Kim, D. (2024).
    Transfer learning for covariance matrix estimation: Optimality and adaptivity.
    Technical report.
  4. Cai, T.T. & Pu, H. (2022).
    Transfer learning for nonparametric regression: Non-asymptotic minimax analysis and adaptive procedure.
    Technical report.
  5. Li, S., Zhang, L., Cai, T.T., & Li, H. (2024).
    Estimation and inference for high-dimensional generalized linear models with knowledge transfer.
    Journal of the American Statistical Association 119, 1274-1285.
  6. Cai, T.T., Kim, D., & Pu, H. (2024).
    Transfer learning for functional mean estimation: Phase transition and adaptive algorithms.
    The Annals of Statistics 52, 654-678.
  7. Cai, C., Cai, T.T., & Li, H. (2024).
    Transfer learning for contextual multi-armed bandits.
    The Annals of Statistics 52, 207-232.
  8. Li, S., Cai, T. T., & Li, H. (2023).
    Transfer learning in large-scale Gaussian graphical models with false discovery rate control.
    Journal of the American Statistical Association 118, 2171-2183.
  9. Li, S., Cai, T. T., & Li, H. (2022).
    Transfer learning for high-dimensional linear regression: Prediction, estimation, and minimax optimality.
    Journal of the Royal Statistical Society, Series B 84, 149-173.

  10. Cai, T. T. & Wei, H. (2021).
    Transfer learning for nonparametric classification: Minimax rate and adaptive classifier.
    The Annals of Statistics 49, 100-128.

     

    Differentially Private Learning

  11. Cai, T. T., Chakraborty, A., & Wang, Y.(2025+).
    Optimal differentially private ranking from pairwise comparisons.
    Journal of the American Statistical Association, to appear.
  12. Auddy, A., Cai, T. T., & Chakraborty, A. (2024).
    Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.
    Technical report.
  13. Cai, T. T., Chakraborty, A., & Vuursteen, L. (2024).
    Federated nonparametric hypothesis testing with differential privacy constraints: Optimal rates and adaptive tests.
    Technical report.
  14. Li, J., Cai, T. T., Xia, D., & Zhang, A. (2024).
    Federated PCA and estimation for spiked covariance matrices: Optimal rates and efficient algorithm.
    Technical report.
  15. Cai, T. T., Xia, D., & Zha, M. (2024).
    Optimal differentially private PCA and estimation for spiked covariance matrices.
    Technical report.
  16. Cai, T. T., Chakraborty, A., & Vuursteen, L. (2024).
    Optimal federated learning for nonparametric regression with heterogenous distributed differential privacy constraints.
    Technical report.
  17. Cai, T. T., Wang, Y., & Zhang, L. (2024).
    Score attack: A lower bound technique for optimal differentially private learning.
    Technical report.
  18. Cai, T. T., Wang, Y., & Zhang, L. (2021).
    The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy.
    The Annals of Statistics 49, 2825-2850.

     

    Federated Learning

  19. Chakraborty, A., Auddy, A., & Cai, T. T. (2025+).
    When data can’t meet: Estimating correlation across privacy barriers.
    NeurIPS, to appear.
  20. Li, J., Cai, T. T., Xia, D., & Zhang, A. (2024).
    Federated PCA and estimation for spiked covariance matrices: Optimal rates and efficient algorithm.
    Technical report.
  21. Cai, T. T., Chakraborty, A., & Vuursteen, L. (2024).
    Optimal federated learning for functional mean estimation under heterogeneous privacy constraints.
    Technical report.
  22. Cai, T. T., Chakraborty, A., & Vuursteen, L. (2024).
    Federated nonparametric hypothesis testing with differential privacy constraints: Optimal rates and adaptive tests.
    Technical report.
  23. Cai, T. T., Chakraborty, A., & Vuursteen, L. (2023).
    Optimal federated learning for nonparametric regression with heterogenous distributed differential privacy constraints.
    Technical report.
  24. Cai, T. T. & Wei, H. (2024).
    Distributed Gaussian mean estimation under communication constraints: Optimal rates and communication-efficient algorithms.
    Journal of Machine Learning Research 25, 1-63.
  25. Cai, T. T. & Wei, H. (2022).
    Distributed adaptive Gaussian mean estimation with unknown variance: Interactive protocols help adaptation.
    The Annals of Statistics 50, 1992-2020.
  26. Cai, T. T. & Wei, H. (2022).
    Distributed nonparametric regression: Optimal rate of convergence and cost of adaptation.
    The Annals of Statistics, 50, 698-725.

     

    Interplay between Statistical Accuracy and Computational Costs

  27. Cai, T. T. & Wu, Y. (2020).
    Statistical and computational limits for sparse matrix detection.
    The Annals of Statistics 48, 1593-1614.

  28. Cai, T. T., Liang, T., & Rakhlin, A. (2017).
    Computational and statistical boundaries for submatrix localization in a large noisy matrix.
    The Annals of Statistics 45, 1403-1430.

     

    High-Dimensional Statistics

     

    PCA, SVD, & Inference for High-Dimensional Low-Rank Matrices

  29. Cai, T. T., Xia, D., & Zha, M. (2024).
    Optimal differentially private PCA and estimation for spiked covariance matrices.
    Technical report.

  30. Cai, T. T., Li, H., & Ma, R. (2021).
    Optimal structured principal subspace estimation: Metric entropy and minimax rates.
    Journal of Machine Learning Research 22, 1-45.

  31. Cai, T. T., Han, X., & Pan, G. (2020).
    Limiting laws for divergent spiked eigenvalues and largest non-spiked eigenvalue of sample covariance matrices.
    The Annals of Statistics 48, 1255-1280.
  32. Zhang, A., Cai, T. T., & Wu, Y. (2022).
    Heteroskedastic PCA: Algorithm, optimality, and applications.
    The Annals of Statistics 50, 53-80.

  33. Cai, T. T. & Zhang, A. (2018).
    Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics.
    The Annals of Statistics, 46, 60-89.

  34. Cai, T. T., Li, X., & Ma, Z. (2016).
    Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow.
    The Annals of Statistics 44, 2221-2251.

  35. Cai, T. T. & Zhou, W. (2016).
    Matrix completion via max-norm constrained optimization.
    Electronic Journal of Statistics 10, 1493-1525.

  36. Cai, T., Cai, T. T., & Zhang, A. (2016).
    Structured matrix completion with applications to genomic data integration.
    Journal of the American Statistical Association 111, 621-633.

  37. Cai, T. T., Liang, T., & Rakhlin, A. (2016).
    Geometric inference for general high-dimensional linear inverse problems.
    The Annals of Statistics 44, 1536–1563.

  38. Cai, T. T., Ma, Z., & Wu, Y. (2015).
    Optimal estimation and rank detection for sparse spiked covariance matrices.
    Probability Theory and Related Fields 161, 781-815.

  39. Cai, T. T. & Zhang, A. (2015).
    ROP: Matrix recovery via rank-one projections.
    The Annals of Statistics 43, 102-138.

  40. Cai, T. T. & Zhang, A. (2014).
    Sparse representation of a polytope and recovery of sparse signals and low-rank matrices.
    IEEE Transactions on Information Theory 60, 122-132.

  41. Cai, T. T., Ma, Z., & Wu, Y. (2013).
    Sparse PCA: Optimal rates and adaptive estimation.
    The Annals of Statistics 41, 3074-3110.

  42. Cai, T. T. & Zhou, W. (2013).
    A max-norm constrained minimization approach to 1-bit matrix completion.
    Journal of Machine Learning Research 14, 3619-3647.

  43. Cai, T. T. & Zhang, A. (2013).
    Compressed sensing and affine rank minimization under restricted isometry.
    IEEE Transactions on Signal Processing 61, 3279-3290.

  44. Cai, T. T. & Zhang, A. (2013).
    Sharp RIP bound for sparse signal and low-rank matrix recovery.
    Applied And Computational Harmonic Analysis 35, 74-93.

     

    Inference for High-Dimensional Covariance Structures

  45. Cai, T. T. & Kim, D. (2024).
    Transfer Learning for Covariance Matrix Estimation: Optimality and Adaptivity.
    Technical report.

  46. Cai, T. T., Li, H., & Ma, R. (2021).
    Optimal structured principal subspace estimation: Metric entropy and minimax rates.
    Journal of Machine Learning Research 22, 1-45.
  47. Zhang, A., Cai, T. T., & Wu, Y. (2022).
    Heteroskedastic PCA: Algorithm, optimality, and applications.
    The Annals of Statistics 50, 53-80.

  48. Cai, T. T., Han, X., & Pan, G. (2020).
    Limiting laws for divergent spiked eigenvalues and largest non-spiked eigenvalue of sample covariance matrices.
    The Annals of Statistics 48, 1255-1280.

  49. Cai, T. T., Hu, J., Li, Y., & Zheng, X. (2020).
    High-dimensional minimum variance portfolio estimation based on high-frequency data.
    The Journal of Econometrics 214, 482-494.

  50. Cai, T. T. & Zhang, A. (2018).
    Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics.
    The Annals of Statistics 46, 60-89.

  51. Xia, Y., Cai, T., & Cai, T. T. (2018).
    Multiple testing of submatrices of a precision matrix with applications to identification of between pathway interactions.
    Journal of the American Statistical Association 113, 328-339.

  52. Cai, T. T. & Zhang, A. (2016).
    Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data.
    Journal of Multivariate Analysis 150, 55-74.

  53. Cai, T. T. & Yuan, M. (2016).
    Minimax and adaptive estimation of covariance operator for random variables observed on a lattice graph.
    Journal of the American Statistical Association 111, 253-265.

  54. Cai, T. T. & Liu, W. (2016).
    Large-scale multiple testing of correlations.
    Journal of the American Statistical Association 111, 229-240.

  55. Cai, T. T., Ren, Z., & Zhou, H. (2016).
    Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (with discussion).
    Electronic Journal of Statistics 10, 1-59.
    (Rejoinder)

  56. Cai, T. T. & Zhang, A. (2016).
    Inference for high-dimensional differential correlation matrices.
    Journal of Multivariate Analysis 143, 107–126.

  57. Cai, T. T., Li, H., Liu, W., & Xie, J. (2016).
    Joint estimation of multiple high-dimensional precision matrices.
    Statistica Sinica 26, 445-464.

  58. Cai, T. T., Liu, W., & Zhou, H. (2016).
    Estimating sparse precision matrix: Optimal rates of convergence and adaptive estimation.
    The Annals of Statistics 44, 455-488.

  59. Xia, Y., Cai, T., & Cai, T. T. (2015).
    Testing differential networks with applications to detecting gene-by-gene interactions.
    Biometrika 102, 247-266.

  60. Cai, T. T., Liang, T., & Zhou, H. (2015).
    Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions.
    Journal of Multivariate Analysis 137, 161-172.

  61. Cai, T. T., Ma, Z., & Wu, Y. (2015).
    Optimal estimation and rank detection for sparse spiked covariance matrices.
    Probability Theory and Related Fields 161, 781-815.

  62. Zhao, S.D., Cai, T. T., & Li, H. (2014).
    Direct estimation of differential networks.
    Biometrika 101, 253-268.

  63. Cai, T. T., Ma, Z., & Wu, Y. (2013).
    Sparse PCA: Optimal rates and adaptive estimation.
    The Annals of Statistics 41, 3074-3110.

  64. Cai, T. T. & Ma, Z. (2013).
    Optimal hypothesis testing for high dimensional covariance matrices.
    Bernoulli 19, 2359-2388.

  65. Cai, T. T., Ren, Z., & Zhou, H. (2013).
    Optimal rates of convergence for estimating Toeplitz covariance matrices.
    Probability Theory and Related Fields 156, 101-143.

  66. Cai, T. T., Liu, W., & Xia, Y. (2013).
    Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings.
    Journal of the American Statistical Association 108, 265-277.

  67. Cai, T. T., Li, H., Liu, W., & Xie, J. (2013).
    Covariate adjusted precision matrix estimation with an application in genetical genomics.
    Biometrika 100, 139-156.

  68. Cai, T. T. & Zhou, H. (2012).
    Optimal rates of convergence for sparse covariance matrix estimation.
    The Annals of Statistics 40, 2389-2420.

  69. Cai, T. T. & Zhou, H. (2012).
    Minimax estimation of large covariance matrices under l1 norm (with discussion).
    Statistica Sinica 22, 1319-1378.

  70. Cai, T. T. & Yuan, M. (2012).
    Adaptive covariance matrix estimation through block thresholding.
    The Annals of Statistics 40, 2014-2042.

  71. Cai, T. T. & Jiang, T. (2012).
    Phase transition in limiting distributions of coherence of high-dimensional random matrices.
    Journal of Multivariate Analysis 107, 24-39.

  72. Cai, T. T. & Liu, W. (2011).
    Adaptive thresholding for sparse covariance matrix estimation.
    Journal of the American Statistical Association 106, 672-684.

  73. Cai, T. T., Liu, W., & Luo, X. (2011).
    A constrained l1 minimization approach to sparse precision matrix estimation.
    Journal of the American Statistical Association 106, 594-607.

  74. Cai, T. T. & Jiang, T. (2011).
    Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices.
    The Annals of Statistics 39, 1496-1525.

  75. Cai, T. T., Zhang, C.-H., & Zhou, H. (2010).
    Optimal rates of convergence for covariance matrix estimation.
    The Annals of Statistics 38, 2118-2144.

     

    Compressed Sensing, High-Dimensional Linear Models & GLMs

  76. Zhang, L., Ma, R., Cai, T. T., & Li, H. (2024).
    Estimation, confidence intervals, and large-scale multiple testing for high-dimensional mixed linear regression.
    Technical report.
  77. Cai, T. T., Wang, Y., & Zhang, L. (2023).
    Score attack: A lower bound technique for optimal differentially private learning.
    Technical report.
  78. Rakshit, R., Wang, Z., Cai, T. T., & Guo, Z.(2024+).
    SIHR: Statistical inference in high-dimensional linear and logistic regression models.
    The R Journal, to appear.
  79. Li, S., Cai, T.T., & Li, H. (2024+).
    Statistical inference for high dimensional regression with proxy data.
    Statistica Sinica, to appear.
  80. Ma, R., Guo, Z., Cai, T. T., & Li, H. (2024).
    Statistical inference for genetic relatedness based on high-dimensional logistic regression.
    Statistica Sinica 34, 1023-1043.
  81. Cai, T. T., Guo, Z., & Xia, Y. (2023).
    Statistical inference and large-scale multiple testing for high-dimensional regression models (with discussion).
    Test 32, 1135-1171.
  82. Cai, T. T., Guo, Z., & Ma, R. (2023).
    Statistical inference for high-dimensional generalized linear models with binary outcomes.
    Journal of the American Statistical Association 118, 1319-1332.
  83. Cai, T., Cai, T. T., & Guo, Z. (2021).
    Optimal statistical inference for individualized treatment effects in high-dimensional models.
    Journal of the Royal Statistical Society, Series B 83, 669-719.
  84. Cai, T. T., Zhang, A., & Zhou, Y. (2022).
    Sparse group Lasso: Sample complexity, optimal rate, and statistical inference.
    IEEE Transactions on Information Theory 68, 5975-6002.

  85. Chakrabortty, A., Lu, J., Cai, T. T., & Li, H. (2019).
    High dimensional M-estimation with missing outcomes: A semi-parametric framework.
    Technical report.
  86. Li, S., Cai, T. T., & Li, H. (2022).
    Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach.
    Journal of the American Statistical Association 117, 1835-1846.
  87. Kang, H., Lee, Y., Cai, T. T., & Small, D. (2022).
    Two robust tools for inference about causal effects with invalid instruments.
    Biometrics 78, 24-34.
  88. Cai, T. T., Wang, Y., & Zhang, L. (2021).
    The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy.
    The Annals of Statistics 49, 2825-2850.
  89. Guo, Z., Renaux, C., Buhlmann, P., & Cai, T. T. (2021).
    Group inference in high dimensions with applications to hierarchical testing.
    Electronic Journal of Statistics 15, 6633-6676.

  90. Cai, T. T. & Guo, Z. (2020).
    Semi-supervised inference for explained variance in high-dimensional regression and its applications.
    Journal of the Royal Statistical Society, Series B 82, 391-419.

  91. Guo, Z., Wang, W., Cai, T. T., & Li, H. (2019).
    Optimal estimation of genetic relatedness in high-dimensional linear models.
    Journal of the American Statistical Association 114, 358-369.

  92. Guo, Z., Kang, H., Cai, T. T., & Small, D. (2018).
    Testing endogeneity with high dimensional covariates.
    The Journal of Econometrics 207, 175-187.

  93. Guo, Z., Kang, H., Cai, T. T., & Small, D. (2018).
    Confidence intervals for causal effects with invalid instruments using two-stage hard thresholding with voting.
    Journal of the Royal Statistical Society, Series B 80, 793-815.

  94. Cai, T. T. & Guo, Z. (2018).
    Accuracy assessment for high-dimensional linear regression.
    The Annals of Statistics 46, 1807-1836.

  95. Xia, Y., Cai, T., & Cai, T. T. (2018).
    Two-sample tests for high-dimensional linear regression with an application to detecting interactions.
    Statistica Sinica 28, 63-92.

  96. Cai, T. T. & Guo, Z. (2017).
    Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity.
    The Annals of Statistics 45, 615-646.

  97. Cai, T. T., Liang, T., & Rakhlin, A. (2016).
    Geometric inference for general high-dimensional linear inverse problems.
    The Annals of Statistics 44, 1536–1563.

  98. Kang, H., Zhang, A., Cai, T. T., & Small, D. (2016).
    Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization.
    Journal of the American Statistical Association 111, 132-144.

  99. Zhao, S.D., Cai, T. T., & Li, H. (2014).
    More powerful genetic association testing via a new statistical framework for integrative genomics.
    Biometrics 70, 881-890.

  100. Cai, T. T. & Yuan, M. (2014).
    Discussion: "A Significant Test for Lasso".
    The Annals of Statistics 42, 478-482.

  101. Cai, T. T. & Zhang, A. (2013).
    Compressed sensing and affine rank minimization under restricted isometry.
    IEEE Transactions on Signal Processing 61, 3279-3290.

  102. Cai, T. T. & Zhang, A. (2013).
    Sharp RIP bound for sparse signal and low-rank matrix recovery.
    Applied And Computational Harmonic Analysis 35, 74-93.

  103. Cai, T. T. & Wang, L. (2011).
    Orthogonal matching pursuit for sparse signal recovery with noise.
    IEEE Transactions on Information Theory 57, 4680-4688.

  104. Cai, T. T. & Jiang, T. (2011).
    Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices.
    The Annals of Statistics 39, 1496-1525.

  105. Cai, T. T., Wang, L., & Xu, G. (2010).
    Stable recovery of sparse signals and an oracle inequality.
    IEEE Transactions on Information Theory 56, 3516-3522.

  106. Cai, T. T., Wang, L., & Xu, G. (2010).
    Shifting inequality and recovery of sparse signals.
    IEEE Transactions on Signal Processing 58, 1300-1308.

  107. Cai, T. T., Xu, G., & Zhang, J. (2009).
    On recovery of sparse signals via l1 minimization.
    IEEE Transactions on Information Theory 55, 3388-3397.

  108. Cai, T. T. & Lv, J. (2007).
    Discussion of "The Dantzig Selector: Statistical estimation when p is much larger than n" by E. Candes and T. Tao.
    The Annals of Statistics 35, 2365-2369.

     

    Supervised, Unsupervised, & Semi-Supervised Learning

  109. Wu, R., Zhang, L., & Cai, T. T. (2025).
    Supervised topic modeling: Optimal estimation and statistical inference.
    Technical report.
  110. Wu, R., Zhang, L., & Cai, T. T. (2023).
    Sparse topic modeling: Computational efficiency, near-optimal algorithms, and statistical inference.
    Journal of the American Statistical Association 118, 1849-1861.
  111. Cai, T. T. & Zhang, L. (2021).
    A convex optimization approach to high-dimensional sparse quadratic discriminant analysis.
    The Annals of Statistics 49, 1537-1568.

  112. Cai, T. T., Ma, J., & Zhang, L. (2019).
    CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality.
    The Annals of Statistics 47, 1234-1267.

  113. Cai, T. T. & Zhang, L. (2019).
    High-dimensional linear discriminant analysis: Optimality, adaptive algorithm, and missing data.
    Journal of the Royal Statistical Society, Series B 81, 675-705.

  114. Zhang, A., Brown, L. D., & Cai, T. T. (2019).
    Semi-supervised inference: General theory and estimation of means.
    The Annals of Statistics, 47, 2538-2566.

  115. Cai, T. T. & Zhang, L. (2016).
    Discussion of "Influential Feature PCA for High Dimensional Clustering".
    The Annals of Statistics 44, 2372-2381.

  116. Cai, T. T. & Liu, W. (2011).
    A direct estimation approach to sparse linear discriminant analysis.
    Journal of the American Statistical Association 106, 1566-1577.

     

    Network Data Analysis

  117. Cai, T. T., Liang, T., & Rakhlin, A. (2020).
    Weighted message passing and minimum energy flow for heterogeneous stochastic block models with side information.
    Journal of Machine Learning Research 21, 1-34.

  118. Cai, T. T., Liang, T., & Rakhlin, A. (2017).
    On detection and structural reconstruction of small world random networks.
    IEEE Transactions on Network Science and Engineering 4, 165-176.

  119. Cai, T. T. & Li, X. (2015).
    Robust and computationally feasible community detection in the presence of arbitrary outlier nodes.
    The Annals of Statistics 43, 1027-1059.

     

    Detection & Identification of Sparse Signals

  120. Cai, T. T. & Wu, Y. (2020).
    Statistical and computational limits for sparse matrix detection.
    The Annals of Statistics 48, 1593-1614.

  121. Zhao, S.D., Cai, T. T., & Li, H. (2017).
    Optimal detection of weak positive latent dependence between two sequences of multiple tests.
    Journal of Multivariate Analysis 160, 169–184.

  122. Zhao, S.D., Cai, T. T., Cappola, T.P., Margulies, K.B., & Li, H. (2017).
    Sparse simultaneous signal detection for identifying genetically controlled disease genes.
    Journal of the American Statistical Association 112, 1032-1046.

  123. Cai, T. T., Liang, T., & Rakhlin, A. (2017).
    Computational and statistical boundaries for submatrix localization in a large noisy matrix.
    The Annals of Statistics 45, 1403-1430.

  124. Cai, T. T. & Sun, W. (2017).
    Optimal screening and discovery of sparse signals with applications to multistage high-throughput studies.
    Journal of the Royal Statistical Society, Series B 79, 197–223.

  125. Jeng, J., Cai, T. T., & Li, H. (2015).
    Sparse segment identifications with applications to DNA copy number variation analysis.
    In Advanced Medical Statistics , 2nd Edition, Y. Lu, J. Fang, L., Tian, and H. Jin, eds., World Scientific, New Jersey, 863-887.

  126. Cai, T. T. & Xia, Y. (2014).
    High-Dimensional Sparse MANOVA.
    Journal of Multivariate Analysis 131, 174-196.

  127. Cai, T. T. & Yuan, M. (2014).
    Rate-optimal detection of very short signal segments.
    Technical report.

  128. Cai, T. T. & Wu, Y. (2014).
    Optimal detection for sparse mixtures against a given null distribution.
    IEEE Transactions on Information Theory 60, 2217-2232.

  129. Cai, T. T., Liu, W., & Xia, Y. (2014).
    Two-sample test of high dimensional means under dependence.
    Journal of the Royal Statistical Society, Series B 76, 349-372.

  130. Jeng, J., Cai, T. T., & Li, H. (2013).
    Simultaneous discovery of rare and common segment variants.
    Biometrika 100, 157-172.

  131. Cai, T. T., Jeng, J., & Li, H. (2012).
    Robust detection and identification of sparse segments in ultra-high dimensional data analysis.
    Journal of the Royal Statistical Society, Series B 74, 773-797.

  132. Cai, T. T., Jeng, J., & Jin, J. (2011).
    Optimal detection of heterogeneous and heteroscedastic mixtures.
    Journal of the Royal Statistical Society, Series B 73, 629-662.

  133. Xie, J., Cai, T. T., & Li, H. (2010).
    Sample size and power analysis for sparse signal recovery in genome-wide association studies.
    Biometrika 98, 273-290.

  134. Jeng, J., Cai, T. T., & Li, H. (2010).
    Optimal sparse segment identification with application in copy number variation analysis.
    Journal of the American Statistical Association 105, 1156-1166.

  135. Cai, T. T. Jin, J., & Low, M. (2007).
    Estimation and confidence sets for sparse normal mixtures.
    The Annals of Statistics 35, 2421-2449.

     

    Other High-Dimensional Problems

  136. Auddy, A., Cai, T. T., & Li, H. (2024).
    Regressing multivariate Gaussian distribution on vector covariates for co-expression network analysis.
    Technical report.
  137. Wang, S., Yuan, B., Cai, T. T., & Li, H. (2024).
    Phylogenetic association analysis with conditional rank correlation.
    Biometrika 111, 881-902.
  138. Cai, T. T., Ke, Z. T., & Turner, P. (2024).
    Testing high-dimensional multinomials with applications to text analysis.
    Journal of the Royal Statistical Society, Series B 86, 922-942.
  139. Cai, T.T. & Ma, R. (2024).
    Matrix reordering for noisy disordered matrices: Optimality and computationally-efficient algorithms.
    IEEE Transactions on Information Theory 70, 509-531.
  140. Cai, T.T. & Ma, R. (2022).
    Theoretical foundations of t-SNE for visualizing high-dimensional clustered data.
    Journal of Machine Learning Research 23, 1-54.
  141. Cai, T. T. Han, R., & Zhang, A. (2022).
    On the non-asymptotic concentration of heteroskedastic wishart-type matrix.
    Electronic Journal of Probability 27, 1-40.
  142. Ma, R., Cai, T. T., & Li, H. (2022).
    Optimal estimation of simultaneous signals using absolute inner product with applications to integrative genomics.
    Statistica Sinica 32, 1027-1048.
  143. Ma, R., Cai, T. T., & Li, H. (2021).
    Optimal estimation of bacterial growth rates based on permuted monotone matrix.
    Biometrika 108, 693–708.
  144. Cai, T. T., Jiang, T., & Li, X. (2021).
    Asymptotic analysis for extreme eigenvalues of principal minors of random matrices.
    The Annals of Applied Probability 31, 2953-2990.
  145. Ma, R., Cai, T. T., & Li, H. (2021).
    Optimal permutation recovery in permuted monotone matrix model.
    Journal of the American Statistical Association 116, 1358-1372.
  146. Wang, S., Cai, T. T., & Li, H. (2021).
    Optimal estimation of Wasserstein distance on a tree with an application to microbiome studies.
    Journal of the American Statistical Association 116, 1237-1253.

  147. Wang, S., Cai, T. T., & Li, H. (2021).
    Hypothesis testing for phylogenetic composition: A minimum-cost flow perspective.
    Biometrika 108, 17-36.

  148. Cai, T. T., Kim, D., Song, X., & Wang, Y. (2021).
    Optimal estimation of eigenspace of large density matrices of quantum systems based on Pauli measurements.
    Journal of Statistical Planning and Inference 213, 50-71.

  149. Cai, T. T. & Zhang, A. (2018).
    Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics.
    The Annals of Statistics 46, 60-89.

  150. Cai, T. T. & Zhang, L. (2018).
    High-dimensional Gaussian copula regression: Adaptive estimation and statistical inference.
    Statistica Sinica 28, 963-993.

  151. Cai, T. T., Liang, T., & Rakhlin, A. (2017).
    Computational and statistical boundaries for submatrix localization in a large noisy matrix.
    The Annals of Statistics 45, 1403-1430.

  152. Cai, T. T., Li, X., & Ma, Z. (2016).
    Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow.
    The Annals of Statistics 44, 2221-2251.

  153. Cai, T. T., Kim, D., Wang, Y., Yuan, M., & Zhou, H. (2016).
    Optimal large-scale quantum state tomography with Pauli measurements.
    The Annals of Statistics 44, 681-712.

  154. Cai, T. T., Eldar, Y. C., & Li, X. (2016).
    Global testing against sparse alternatives in time-frequency analysis.
    The Annals of Statistics 44, 1438–1466.

  155. Cai, T. T., Liang, T., & Rakhlin, A. (2016).
    Geometric inference for general high-dimensional linear inverse problems.
    The Annals of Statistics 44, 1536–1563.

  156. Cai, T. T. & Xia, Y. (2014).
    High-Dimensional Sparse MANOVA.
    Journal of Multivariate Analysis 131, 174-196.

  157. Cai, T. T., Liu, W., & Xia, Y. (2014).
    Two-sample test of high dimensional means under dependence.
    Journal of the Royal Statistical Society, Series B 76, 349-372.

  158. Cai, T. T., Fan, J., & Jiang, T. (2013).
    Distributions of angles in random packing on spheres.
    Journal of Machine Learning Research 14, 1837-1864.

  159. Cai, T. T. & Jiang, T. (2012).
    Phase transition in limiting distributions of coherence of high-dimensional random matrices.
    Journal of Multivariate Analysis 107, 24-39.

  160. Cai, T. T. & Jiang, T. (2011).
    Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices.
    The Annals of Statistics 39, 1496-1525.

     

    Large-Scale Multiple Testing

  161. Xia, Y. & Cai, T. T. (2023).
    Discussion of "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models" by Dai, Lin, Xing, and Liu
    Journal of the American Statistical Association 118, 1569-1572.
  162. Liang, Z., Cai, T.T., Sun, W., & Xia, Y. (2024+).
    A locally adaptive algorithm for multiple testing with network structure.
    Statistica Sinica, to appear.
  163. Cai, T. T., Sun, W., & Xia, Y. (2022).
    LAWS: A locally adaptive weighting and screening approach to spatial multiple testing.
    Journal of the American Statistical Association 117, 1370-1383.

  164. Ma, R., Cai, T. T., & Li, H. (2021).
    Global and simultaneous testing for high-dimensional logistic regression models.
    Journal of the American Statistical Association 116, 984-998.

  165. Xia, Y., Cai, T. T., & Sun, W. (2020).
    GAP: A general framework for information pooling in two-sample sparse inference.
    Journal of the American Statistical Association 115, 1236-1250.

  166. Cai, T. T., Li, H., Ma, J., & Xia, Y. (2019).
    Differential Markov random field analysis with an application to detecting differential microbial community networks.
    Biometrika 106, 401-416.

  167. Xiang, D., Zhao, S. D., & Cai, T. T. (2019).
    Signal classification for the integrative analysis of multiple sequences of multiple tests.
    Journal of the Royal Statistical Society, Series B 81, 707-734.

  168. Cai, T. T., Sun, W., & Wang, W. (2019).
    CARS: Covariate assisted ranking and screening for large-scale two-sample inference (with discussion).
    Journal of the Royal Statistical Society, Series B 81, 187-234.

  169. Cai, T., Cai, T. T., Liao, K., & Liu, W. (2019).
    Large-scale simultaneous testing of cross-covariance matrix with applications to PheWAS.
    Statistica Sinica 29, 983-1005.

  170. Basu, P., Cai, T. T., Das, K., & Sun, W. (2018).
    Weighted false discovery rate control in large-scale multiple testing.
    Journal of the American Statistical Association 113, 1172-1183.

  171. Xia, Y., Cai, T. T. & Li, H. (2018).
    Joint testing and false discovery rate control for high-dimensional multivariate regression.
    Biometrika 105, 249-269.

  172. Xia, Y., Cai, T., & Cai, T. T. (2018).
    Multiple testing of submatrices of a precision matrix with applications to identification of between pathway interactions.
    Journal of the American Statistical Association 113, 328-339.

  173. Xia, Y., Cai, T., & Cai, T. T. (2018).
    Two-sample tests for high-dimensional linear regression with an application to detecting interactions.
    Statistica Sinica 28, 63-92.

  174. Cai, T. T. & Sun, W. (2017).
    Large-scale global and simultaneous inference: Estimation and testing in very high dimensions.
    Annual Review of Economics 9, 411-439.

  175. Cai, T. T. (2017).
    Global testing and large-scale multiple testing for high-dimensional covariance structures.
    Annual Review of Statistics and Its Applications 4, 423–446.

  176. Cai, T. T. & Liu, W. (2016).
    Large-scale multiple testing of correlations.
    Journal of the American Statistical Association111, 229-240.

  177. Xia, Y., Cai, T., & Cai, T. T. (2015).
    Testing differential networks with applications to detecting gene-by-gene interactions.
    Biometrika 102, 247-266.

  178. Sun, W., Reich, B. J., Cai, T. T., Guindani, M., & Schwartzman, A. (2015).
    False discovery control in large-scale spatial multiple testing.
    Journal of the Royal Statistical Society, Series B 77, 59-83.

  179. Xie, J., Cai, T. T., Maris, J. & Li, H. (2011).
    Optimal false discovery rate control for dependent data.
    Statistics and Its Interface 4, 417-430.
  180. Cai, T. T. & Sun, W. (2011).
    A compound decision-theoretic approach to large-scale multiple testing.
    In High-dimensional Data Analysis, pages 75–116. World Scientific.

  181. Cai, T. T. & Jin, J. (2010).
    Optimal rates of convergence for estimating the null and proportion of non-null effects in large-scale multiple testing.
    The Annals of Statistics 38, 100-145.

  182. Cai, T. T. & Sun, W. (2010).
    A compound decision-theoretic approach to large-scale multiple testing.
    In High-Dimensional Data Analysis , T. T. Cai and X. Shen, eds., World Scientific, New Jersey, 75-116.

  183. Cai, T. T. (2010).
    Comments on "Correlated z-values and the Accuracy of Large-Scale Statistical Estimates" by Bradley Efron.
    Journal of the American Statistical Association 105, 1055-1056.

  184. Cai, T. T. & Sun, W. (2009).
    Simultaneous testing of grouped hypotheses: Finding needles in multiple haystacks.
    Journal of the American Statistical Association 104, 1467 - 1481.

  185. Sun, W. & Cai, T. T. (2009).
    Large-scale multiple testing under dependency.
    Journal of the Royal Statistical Society, Series B 71, 393-424.

  186. Sun, W. & Cai, T. T. (2007).
    Oracle and adaptive compound decision rules for false discovery rate control.
    Journal of the American Statistical Association 102, 901-912.

  187. Jin, J. & Cai, T. T. (2007).
    Estimating the null and the proportion of non-null effects in large-scale multiple comparisons.
    Journal of the American Statistical Association 102, 495-506.

  188. Cai, T. T. (2008).
    Discussion of "Microarrays, Empirical Bayes, and the Two-Group Model" by Bradley Efron.
    Statistical Science 23, 29-33.

     

    Functional Data Analysis

  189. Cai, T.T., Kim, D., & Pu, H. (2023).
    Transfer learning for functional mean estimation: Phase transition and adaptive algorithms.
    Technical report.

  190. Cai, T. T., Zhang, L., & Zhou, H. H. (2018).
    Adaptive functional linear regression via functional principal component analysis and block thresholding.
    Statistica Sinica 28, 2455-2468.

  191. Cai, T. T. & Yuan, M. (2012).
    Minimax and adaptive prediction for functional linear regression.
    Journal of the American Statistical Association 107, 1201-1216.

  192. Cai, T. T. & Yuan, M. (2011).
    Optimal estimation of the mean function based on discretely sampled functional data: Phase transition.
    The Annals of Statistics 39, 2330-2355.

  193. Cai, T. T. & Yuan, M. (2010).
    Nonparametric covariance function estimation for functional and longitudinal data.
    Technical Report.

  194. Yuan, M. & Cai, T. T. (2010).
    A reproducing kernel Hilbert space approach to functional linear regression.
    The Annals of Statistics 38, 3412-3444.

  195. Cai, T. T. & Hall, P. (2006).
    Prediction in functional linear regression.
    The Annals of Statistics 34, 2159-2179.

     

    Theory & Methodology for Nonparametric Function Estimation

  196. Cai, T.T., Chen, R., & Zhu, Y. (2024).
    Estimation and inference for minimizer and minimum of convex functions: Optimality, adaptivity, and uncertainty principles.
    The Annals of Statistics 52, 392-411.
  197. Cai, T.T. & Pu, H. (2022).
    Stochastic continuum-armed bandits with additive models: Minimax regrets and adaptive algorithm.
    The Annals of Statistics 50, 2179-2204.

  198. Cai, T. T. & Wei, H. (2021).
    Transfer learning for nonparametric classification: Minimax rate and adaptive classifier.
    The Annals of Statistics 49, 100-128.

  199. Cai, T. T. (2019).
    Gaussianization Machines for non-Gaussian function estimation models.
    Statistical Science 34, 635–656.

  200. Cai, T. T., Guntuboyina, A., & Wei, Y. (2018).
    Adaptive estimation of planar convex sets.
    The Annals of Statistics 46, 1018-1049.

  201. Cai, T. T. & Low, M. (2015).
    A framework for estimation of convex functions.
    Statistica Sinica 25, 423-456.

  202. Cai, T. T., Low, M., & Ma, Z. (2014).
    Adaptive confidence bands for nonparametric regression functions.
    Journal of the American Statistical Association 109, 1054-1070.

  203. Cai, T. T., Low, M., & Xia, Y. (2013).
    Adaptive confidence intervals for regression functions under shape constraints.
    The Annals of Statistics 41, 722-750.

  204. Cai, T. T. (2012).
    Minimax and adaptive inference in nonparametric function estimation.
    Statistical Science 27, 31-50.

  205. Wang, L., Brown, L.D., & Cai, T. T. (2011).
    A difference based approach to the semiparametric partial linear model.
    Electronic Journal of Statistics 5, 619-641.

  206. Cai, T. T. & Zhou, H. (2010).
    Nonparametric regression in natural exponential families.
    In Borrowing Strength: Theory Powering Applications -- A Festschrift for Lawrence D. Brown, IMS Collections Vol. 6, 199-215.

  207. Cai, T. T. & Zhou, H. (2009).
    Asymptotic equivalence and adaptive estimation for robust nonparametric regression.
    The Annals of Statistics 37, 3204-3235.

  208. Cai, T. T. (2008).
    On information pooling, adaptability and superefficiency in nonparametric function estimation.
    Journal of Multivariate Analysis 99, 412-436.

  209. Cai, T. T., Low, M., & Zhao, L. (2007).
    Tradeoffs between global and local risks in nonparametric function estimation.
    Bernoulli 13, 1-19.

  210. Cai, T. T. & Hall, P. (2006).
    Prediction in functional linear regression.
    The Annals of Statistics 34, 2159-2179.

  211. Cai, T. T. & Low, M. (2005).
    Nonparametric function estimation over shrinking neighborhoods: Superefficiency and adaptation.
    The Annals of Statistics 33, 184-213.

  212. Cai, T. T. (2003).
    Rates of convergence and adaptation over Besov spaces under pointwise risk.
    Statistica Sinica 13, 881-902.

  213. Brown, L.D., Cai, T. T., Low, M.G., & Zhang, C. (2002).
    Asymptotic equivalence theory for nonparametric regression with random design.
    The Annals of Statistics 30, 688-707.

     

    Wavelet Thresholding

  214. Cai, T. T. & Zhou, H. (2010).
    Nonparametric regression in natural exponential families.
    In Borrowing Strength: Theory Powering Applications -- A Festschrift for Lawrence D. Brown, IMS Collections Vol. 6, 199-215.

  215. Brown, L. D., Cai, T. T., & Zhou, H. (2010).
    Nonparametric regression in exponential families.
    The Annals of Statistics 38, 2005-2046.

  216. Brown, L. D., Cai, T. T., Zhang, R., Zhao, L., & Zhou, H. (2010).
    The root-unroot algorithm for density estimation as implemented via wavelet block thresholding.
    Probability Theory and Related Fields 146, 401-433.

  217. Cai, T. T. & Zhou, H. (2009).
    Asymptotic equivalence and adaptive estimation for robust nonparametric regression.
    The Annals of Statistics 37, 3204-3235.

  218. Cai, T. T. & Zhou, H. (2009).
    A data-driven block thresholding approach to wavelet estimation.
    The Annals of Statistics 37, 569-595.
  219. Cai, T. T., Low, M., & Zhao, L. (2009).
    Sharp adaptive estimation by a blockwise method.
    Journal of Nonparametric Statistics 21, 839-850.

  220. Cai, T. T. & Wang, L. (2008).
    Adaptive variance function estimation in heteroscedastic nonparametric regression.
    The Annals of Statistics 36, 2025-2054.

  221. Brown, L. D., Cai, T. T., & Zhou, H. (2008).
    Robust nonparametric estimation via wavelet median regression.
    The Annals of Statistics 36, 2055-2084.

  222. Chicken, E. & Cai, T. T. (2005).
    Block thresholding for density estimation: Local and global adaptivity.
    Journal of Multivariate Analysis 95, 76-106.

  223. Cai, T. T. (2003).
    Rates of convergence and adaptation over Besov spaces under pointwise risk.
    Statistica Sinica 13, 881-902.

  224. Cai, T. T. (2002).
    On adaptive wavelet estimation of a derivative and other related linear inverse problems.
    J. Statistical Planning and Inference 108, 329-349.

  225. Cai, T. T. (2002).
    On block thresholding in wavelet regression: Adaptivity, block size, and threshold level.
    Statistica Sinica 12, 1241-1273.

  226. Cai, T. T. (2001).
    Discussion on "regularization of wavelets approximations" by A. Antoniadis and J. Fan.
    Journal of the American Statistical Association 96, 960-962.

  227. Cai, T. T., Zhang, D., & Ben-Amotz, D. (2001).
    Enhanced chemical classification of Raman images using wavelet transformation.
    Applied Spectroscopy 55, 1124-1130.

  228. Cai, T. T. & Silverman, B.W. (2001).
    Incorporating information on neighboring coefficients into wavelet estimation.
    Sankhya 63, 127-148.

  229. Cai, T. T. (1999).
    Adaptive wavelet estimation: a block thresholding and oracle inequality approach.
    The Annals of Statistics 27, 898-924.

  230. Cai, T. T. & Brown, L.D. (1999).
    Wavelet estimation for samples with random uniform design.
    Statistics and Probability Letters 42, 313-321.

  231. Cai, T. T. & Brown, L.D. (1998).
    Wavelet shrinkage for nonequispaced samples.
    The Annals of Statistics 26, 1783-1799.

     

    Variance Function Estimation

  232. Cai, T. T., Munk, A., & Schmidt-Hieber, J. (2010).
    Sharp minimax estimation of the variance of Brownian motion corrupted with Gaussian noise.
    Statistica Sinica 20, 1011-1024.

  233. Cai, T. T., Levine, M., & Wang, L. (2009).
    Variance function estimation in multivariate nonparametric regression.
    Journal of Multivariate Analysis 100, 126-136.

  234. Cai, T. T. & Wang, L. (2008).
    Adaptive variance function estimation in heteroscedastic nonparametric regression.
    The Annals of Statistics 36, 2025-2054.

  235. Wang, L., Brown, L. D., Cai, T. T., & Levine, M. (2008).
    Effect of mean on variance function estimation in nonparametric regression.
    The Annals of Statistics 36, 646-664.

     

    Inference for Linear, Quadratic, & Nonsmooth Functionals

  236. Cai, T. T. & Guo, Z. (2020).
    Semi-supervised inference for explained variance in high-dimensional regression and its applications.
    Journal of the Royal Statistical Society, Series B 82, 391-419.

  237. Cai, T. T. & Guo, Z. (2018).
    Accuracy assessment for high-dimensional linear regression.
    The Annals of Statistics 46, 1807-1836.

  238. Cai, T. T. & Guo, Z. (2017).
    Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity.
    The Annals of Statistics 45, 615-646.

  239. Cai, T. T. & Tan, X. L. (2017).
    Optimal estimation of a quadratic functional under the Gaussian two-sequence model.
    Statistica Sinica 27, 879-906.

  240. Cai, T. T., Low, M., & Ma, Z. (2014).
    Adaptive confidence bands for nonparametric regression functions.
    Journal of the American Statistical Association 109, 1054-1070.

  241. Cai, T. T., Low, M., & Xia, Y. (2013).
    Adaptive confidence intervals for regression functions under shape constraints.
    The Annals of Statistics 41, 722-750.

  242. Cai, T. T. (2012).
    Minimax and adaptive inference in nonparametric function estimation.
    Statistical Science 27, 31-50.

  243. Cai, T. T. & Low, M. (2011).
    Testing composite hypotheses, Hermite polynomials, & optimal estimation of a nonsmooth functional.
    The Annals of Statistics 39, 1012-1041.

  244. Cai, T. T. & Low, M. (2006).
    Optimal adaptive estimation of a quadratic functional.
    The Annals of Statistics 34, 2298-2325.

  245. Cai, T. T. & Low, M. (2006).
    Adaptive confidence balls.
    The Annals of Statistics 34, 202-228.

  246. Cai, T. T. & Low, M. (2006).
    Adaptation under probabilistic error for estimating linear functionals.
    Journal of Multivariate Analysis 97, 231-245.

  247. Cai, T. T. & Low, M. (2005).
    Non-quadratic estimators of a quadratic functional.
    The Annals of Statistics 33, 2930-2956.

  248. Cai, T. T. & Low, M. (2005).
    On adaptive estimation of linear functionals.
    The Annals of Statistics 33, 2311-2343.

  249. Cai, T. T. & Low, M. (2005).
    Adaptive estimation of linear functionals under different performance measures.
    Bernoulli 11, 341-358.

  250. Cai, T. T. & Low, M. (2004).
    An adaptation theory for nonparametric confidence intervals.
    The Annals of Statistics 32, 1805-1840.

  251. Cai, T. T. & Low, M. (2004).
    Minimax estimation of linear functionals over nonconvex parameter spaces.
    The Annals of Statistics 32, 552 - 576.

  252. Cai, T. T. & Low, M. (2003).
    A note on nonparametric estimation of linear functionals.
    The Annals of Statistics 31, 1140-1153.

  253. Cai, T. T. & Low, M. (2002).
    On modulus of continuity and adaptability in nonparametric functional estimation.
    Technical Report.

     

    Uncertainty Quantification

  254. Cai, T. T. & Guo, Z. (2018).
    Accuracy assessment for high-dimensional linear regression.
    The Annals of Statistics 46, 1807-1836.

  255. Cai, T. T. & Guo, Z. (2017).
    Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity.
    The Annals of Statistics 45, 615-646.

  256. Cai, T. T., Low, M., & Ma, Z. (2014).
    Adaptive confidence bands for nonparametric regression functions.
    Journal of the American Statistical Association 109, 1054-1070.

  257. Cai, T. T., Low, M., & Xia, Y. (2013).
    Adaptive confidence intervals for regression functions under shape constraints.
    The Annals of Statistics 41, 722-750.

  258. Cai, T. T. & Low, M. (2006).
    Adaptive confidence balls.
    The Annals of Statistics 34, 202-228.

  259. Cai, T. T. & Low, M. (2004).
    An adaptation theory for nonparametric confidence intervals.
    The Annals of Statistics 32, 1805-1840.

  260. Brown, L.D., Cai, T. T., & DasGupta, A. (2003).
    Interval estimation in exponential families.
    Statistica Sinica 13, 19-49.

  261. Brown, L.D., Cai, T. T., & DasGupta, A. (2002).
    Confidence intervals for a binomial proportion and asymptotic expansions.
    The Annals of Statistics 30, 160-201.

  262. Brown, L.D., Cai, T. T., & DasGupta, A. (2001).
    Interval estimation for a binomial proportion (with discussion).
    Statistical Science 16, 101-133.

     

    Inference for Discrete Distributions

  263. Cai, T. T. & Wang, H. (2009).
    Tolerance intervals for discrete distributions in exponential families.
    Statistica Sinica 19, 905-923.

  264. Cai, T. T. (2005).
    One-sided confidence intervals in discrete distributions.
    J. Statistical Planning and Inference 131, 63-88.

  265. Brown, L.D., Cai, T. T., & DasGupta, A. (2005).
    Discussion of "fuzzy and randomized confidence intervals and p-values" by C. J. Geyer and G. D. Meeden.
    Statistical Science 20, 375-379.

  266. Brown, L.D., Cai, T. T., & DasGupta, A. (2003).
    Interval estimation in exponential families.
    Statistica Sinica 13, 19-49.

  267. Brown, L.D., Cai, T. T., & DasGupta, A. (2002).
    Confidence intervals for a binomial proportion and asymptotic expansions.
    The Annals of Statistics 30, 160-201.

  268. Brown, L.D., Cai, T. T., & DasGupta, A. (2001).
    Interval estimation for a binomial proportion (with discussion).
    Statistical Science 16, 101-133.

     

    Applications

  269. Tan, J., Zhang, Y., Hong, C., Cai, T.T., Cai, T., and Zhang, A. (2025).
    Integrated analysis for electronic health records with structured and sporadic missingness.
    Journal of Biomedical Informatics 171, 104933.
  270. Liao, K. P., Sparks, J. A., Hejblum, B., Kuo, I-H., Cui, J., Lahey, L., Cagan, A., Gainer, V., Liu, W., Cai, T. T., Sokolove, J., & Cai, T. (2017).
    Phenome-wide association study of autoantibodies to citrullinated and non-citrullinated epitopes in rheumatoid arthritis.
    Arthritis & Rheumatology 69, 742-749.

  271. Jeng, J., Cai, T. T., & Li, H. (2013).
    Simultaneous discovery of rare and common segment variants.
    Biometrika 100, 157-172.

  272. Cai, T. T., Li, H., Liu, W., & Xie, J. (2013).
    Covariate adjusted precision matrix estimation with an application in genetical genomics.
    Biometrika 100, 139-156.

  273. Cai, T. T., Jeng, J., & Li, H. (2012).
    Robust detection and identification of sparse segments in ultra-high dimensional data analysis.
    Journal of the Royal Statistical Society, Series B 74, 773-797.

  274. Xie, J., Cai, T. T., & Li, H. (2010).
    Sample size and power analysis for sparse signal recovery in genome-wide association studies.
    Biometrika 98, 273-290.

  275. Jeng, J., Cai, T. T., & Li, H. (2010).
    Optimal sparse segment identification with application in copy number variation analysis.
    Journal of the American Statistical Association 105, 1156-1166.

  276. Jin, J. & Cai, T. T. (2007).
    Estimating the null and the proportion of non-null effects in large-scale multiple comparisons.
    Journal of the American Statistical Association 102, 495-506.

  277. Sun, W. & Cai, T. T. (2007).
    Oracle and adaptive compound decision rules for false discovery rate control.
    Journal of the American Statistical Association 102, 901-912.

  278. Cai, T. T., Zhang, D., & Ben-Amotz, D. (2001).
    Enhanced chemical classification of Raman images using wavelet transformation.
    Applied Spectroscopy 55, 1124-1130.

  279. Shyu, C-R., Cai, T. T., & Broderick, L.S. (1999).
    On archiving and retrieval of sequential images from tomographic databases in PACS.
    Proc. SPIE - the International Society for Optical Engineering 3656, 33-40.
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