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2. Large Dimensional Data Analysis using Orthogonally Decomposable Tensors: Statistical Optimality and Computational Tractability
3. Martingale Schrodinger Bridges and Optimal Semistatic Portfolios
4. On the Multiway Principal Component Analysis
5. Process Data Applications in Educational Assessment
6. Statistical Methods for Structured Data: Analyses of Discrete Time Series and Networks
7. Advances in Machine Learning for Complex Structured Functional Data
8. Advances in Machine Learning for Compositional Data
9. Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing
10. Latent Variable Models for Events on Social Networks
11. Modeling Random Events
12. Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling
13. On Recovering the Best Rank-? Approximation from Few Entries
14. Overlapping Communities on Large-Scale Networks: Benchmark Generation and Learning via Adaptive Stochastic Optimization
15. Statistical approach to tagging stellar birth groups in the Milky Way
16. Statistical Perspectives on Modern Network Embedding Methods
17. Advances in Statistical Machine Learning Methods for Neural Data Science
18. Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles
19. Event History Analysis in Multivariate Longitudinal Data
20. High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices
21. On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods
22. Phase retrieval in the high-dimensional regime
23. Semiparametric Inference of Censored Data with Time-dependent Covariates
24. Statistical Learning for Process Data
25. Toward a scalable Bayesian workflow
26. Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates
27. Deep Probabilistic Graphical Modeling
28. High-dimensional asymptotics: new insights and methods
29. Latent Variable Models in Measurement: Theory and Application
30. Multiple Causal Inference with Bayesian Factor Models
31. New perspectives in cross-validation
32. Partition-based Model Representation Learning
33. Some Statistical Models for Prediction
34. Statistical Analysis of Complex Data in Survival and Event History Analysis
35. Time evolution of the Kardar-Parisi-Zhang equation
36. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience
37. Essays in High Dimensional Time Series Analysis
38. Limit theorems beyond sums of I.I.D observations
39. Linear Constraints in Optimal Transport
40. Meta-analysis of expression and methylation signatures indicates a stress-related epigenetic mechanism in multiple neuropsychiatric disorders
41. Point Process Models for Heterogeneous Event Time Data
42. Scalable Community Detection in Massive Networks using Aggregated Relational Data
43. Analysis of genotype by methylation interactions through sparsity-inducing regularized regression
44. Application of Distance Covariance to Extremes and Time Series and Inference for Linear Preferential Attachment Networks
45. Bayesian hierarchical vector autoregressive models for patient-level predictive modeling
46. Bayesian Modeling Strategies for Complex Data Structures, with Applications to Neuroscience and Medicine
47. Bifurcation analysis of two coupled Jansen-Rit neural mass models
48. Causal modeling in a multi-omic setting: insights from GAW20
49. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data
50. Coping with family structure in genome-wide association studies: a comparative evaluation
51. Different population dynamics in the supplementary motor area and motor cortex during reaching
52. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
53. Essays in Cluster Sampling and Causal Inference
54. Minimax-inspired Semiparametric Estimation and Causal Inference
55. Neyman-Pearson classification algorithms and NP receiver operating characteristics
56. Readmission prediction via deep contextual embedding of clinical concepts
57. Scoring Model Predictions using Cross-Validation
58. Selected Legal Applications for Bayesian Methods
59. Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
60. Stochastic Differential Equations and Strict Local Martingales
61. The challenge of detecting genotype-by-methylation interaction: GAW20
62. Topics in Computational Bayesian Statistics With Applications to Hierarchical Models in Astronomy and Sociology
63. A continuous morphological approach to study the evolution of pollen in a phylogenetic context: An example with the order Myrtales
64. Advances in Credit Risk Modeling
65. A unified view of high-dimensional bridge regression
66. Body size and hosts of Triatoma infestans populations affect the size of bloodmeal contents and female fecundity in rural northwestern Argentina
67. Distributionally Robust Optimization and its Applications in Machine Learning
68. Distributionally Robust Performance Analysis with Applications to Mine Valuation and Risk
69. Efficient Estimation of the Expectation of a Latent Variable in the Presence of Subject-Specific Ancillaries
70. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays
71. Empirical Bayes, Bayes factors and deoxyribonucleic acid fingerprinting
72. Essays on Matching and Weighting for Causal Inference in Observational Studies
73. Expansion of a filtration with a stochastic process: a high frequency trading perspective
74. Fast online deconvolution of calcium imaging data
75. Flexible Sparse Learning of Feature Subspaces
76. Multi-scale approaches for high-speed imaging and analysis of large neural populations
77. Random Walk Models, Preferential Attachment, and Sequential Monte Carlo Methods for Analysis of Network Data
78. Statistical Machine Learning Methods for High-dimensional Neural Population Data Analysis
79. The Prior Can Often Only Be Understood in the Context of the Likelihood
80. Time Series Modeling with Shape Constraints
81. Advances in Model Selection Techniques with Applications to Statistical Network Analysis and Recommender Systems
82. Asymptotic Theory and Applications of Random Functions
83. Latent Variable Modeling and Statistical Learning
84. Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen
85. Measuring Spatial Extremal Dependence
86. Methods for Personalized and Evidence Based Medicine
87. On Model-Selection and Applications of Multilevel Models in Survey and Causal Inference
88. R codes for "Sparse Vector Autoregressive Modeling" by Davis, Zang and Zheng (2016)
89. Semiparametric inference with shape constraints
90. Spectral Filtering for Spatio-temporal Dynamics and Multivariate Forecasts
91. Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1
92. A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery
93. A Graphon-based Framework for Modeling Large Networks
94. An Assortment of Unsupervised and Supervised Applications to Large Data
95. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
96. Efficiency in Lung Transplant Allocation Strategies
97. GLMLE: graph-limit enabled fast computation for fitting exponential random graph models to large social networks
98. Human and Machine Learning in Non-Markovian Decision Making
99. On Identifying Rare Variants for Complex Human Traits
100. A dual-clustering framework for association screening with whole genome sequencing data and longitudinal traits
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