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2. Application of Distance Covariance to Time Series Modeling and Assessing Goodness-of-Fit
3. Nonparametric Methods for Measuring Conditional Dependence, Multi-Sample Dissimilarity, and Testing for Symmetry
4. On Modeling Spatial Time-to-Event Data with Missing Censoring Type
5. On Some Problems In Transfer Learning
6. Semiparametric Modeling and Analysis for Time-varying Network Data
7. Topics on Machine Learning under Imperfect Supervision
8. Trade-Offs and Opportunities in High-Dimensional Bayesian Modeling
9. Inference in ERGMs and Ising Models.
10. Large Dimensional Data Analysis using Orthogonally Decomposable Tensors: Statistical Optimality and Computational Tractability
11. Martingale Schrodinger Bridges and Optimal Semistatic Portfolios
12. On the Multiway Principal Component Analysis
13. Optimal Inference with a Multidimensional Multiscale Statistic
14. Process Data Applications in Educational Assessment
15. Signal-to-noise ratio aware minimaxity and its asymptotic expansion
16. Statistical Methods for Structured Data: Analyses of Discrete Time Series and Networks
17. Advances in Machine Learning for Complex Structured Functional Data
18. Advances in Machine Learning for Compositional Data
19. Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing
20. Latent Variable Models for Events on Social Networks
21. Modeling Random Events
22. Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling
23. On Recovering the Best Rank-? Approximation from Few Entries
24. Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via Adaptive Stochastic Optimization
25. Statistical approach to tagging stellar birth groups in the Milky Way
26. Statistical Perspectives on Modern Network Embedding Methods
27. Advances in Statistical Machine Learning Methods for Neural Data Science
28. Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles
29. Event History Analysis in Multivariate Longitudinal Data
30. High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices
31. On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods
32. Phase retrieval in the high-dimensional regime
33. Semiparametric Inference of Censored Data with Time-dependent Covariates
34. Statistical Learning for Process Data
35. Toward a scalable Bayesian workflow
36. Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates
37. Deep Probabilistic Graphical Modeling
38. High-dimensional asymptotics: new insights and methods
39. Latent Variable Models in Measurement: Theory and Application
40. Multiple Causal Inference with Bayesian Factor Models
41. New perspectives in cross-validation
42. Partition-based Model Representation Learning
43. Some Statistical Models for Prediction
44. Statistical Analysis of Complex Data in Survival and Event History Analysis
45. Time evolution of the Kardar-Parisi-Zhang equation
46. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience
47. Essays in High Dimensional Time Series Analysis
48. Limit theorems beyond sums of I.I.D observations
49. Linear Constraints in Optimal Transport
50. Meta-analysis of expression and methylation signatures indicates a stress-related epigenetic mechanism in multiple neuropsychiatric disorders
51. Point Process Models for Heterogeneous Event Time Data
52. Scalable Community Detection in Massive Networks using Aggregated Relational Data
53. Analysis of genotype by methylation interactions through sparsity-inducing regularized regression
54. Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks
55. Bayesian hierarchical vector autoregressive models for patient-level predictive modeling
56. Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine
57. Bifurcation analysis of two coupled Jansen-Rit neural mass models
58. Causal modeling in a multi-omic setting: insights from GAW20
59. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data
60. Coping with family structure in genome-wide association studies: a comparative evaluation
61. Different population dynamics in the supplementary motor area and motor cortex during reaching
62. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
63. Essays in Cluster Sampling and Causal Inference
64. Minimax-inspired Semiparametric Estimation and Causal Inference
65. Neyman-Pearson classification algorithms and NP receiver operating characteristics
66. Readmission prediction via deep contextual embedding of clinical concepts
67. Scoring Model Predictions using Cross-Validation
68. Selected Legal Applications for Bayesian Methods
69. Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
70. Stochastic Differential Equations and Strict Local Martingales
71. The challenge of detecting genotype-by-methylation interaction: GAW20
72. Topics in Computational Bayesian Statistics With Applications to Hierarchical Models in Astronomy and Sociology
73. A continuous morphological approach to study the evolution of pollen in a phylogenetic context: An example with the order Myrtales
74. Advances in Credit Risk Modeling
75. A unified view of high-dimensional bridge regression
76. Body size and hosts of Triatoma infestans populations affect the size of bloodmeal contents and female fecundity in rural northwestern Argentina
77. Distributionally Robust Optimization and its Applications in Machine Learning
78. Distributionally Robust Performance Analysis with Applications to Mine Valuation and Risk
79. Efficient Estimation of the Expectation of a Latent Variable in the Presence of Subject-Specific Ancillaries
80. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays
81. Empirical Bayes, Bayes factors and deoxyribonucleic acid fingerprinting
82. Essays on Matching and Weighting for Causal Inference in Observational Studies
83. Expansion of a filtration with a stochastic process: a high frequency trading perspective
84. Fast online deconvolution of calcium imaging data
85. Flexible Sparse Learning of Feature Subspaces
86. Multi-scale approaches for high-speed imaging and analysis of large neural populations
87. Random Walk Models, Preferential Attachment, and Sequential Monte Carlo Methods for Analysis of Network Data
88. Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis
89. The Prior Can Often Only Be Understood in the Context of the Likelihood
90. Time Series Modeling with Shape Constraints
91. Advances in Model Selection Techniques with Applications to Statistical Network Analysis and Recommender Systems
92. Asymptotic Theory and Applications of Random Functions
93. Latent Variable Modeling and Statistical Learning
94. Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen
95. Measuring Spatial Extremal Dependence
96. Methods for Personalized and Evidence Based Medicine
97. On Model-Selection and Applications of Multilevel Models in Survey and Causal Inference
98. R codes for "Sparse Vector Autoregressive Modeling" by Davis, Zang and Zheng (2016)
99. Semiparametric inference with shape constraints
100. Spectral Filtering for Spatio-temporal Dynamics and Multivariate Forecasts
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