[图书][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …
An overview of tests on high-dimensional means
Testing high-dimensional means has many applications in scientific research. For instance,
it is of great interest to test whether there is a difference of gene expressions between control …
it is of great interest to test whether there is a difference of gene expressions between control …
Are latent factor regression and sparse regression adequate?
Abstract We propose the Factor Augmented (sparse linear) Regression Model (FARM) that
not only admits both the latent factor regression and sparse linear regression as special …
not only admits both the latent factor regression and sparse linear regression as special …
Dynamic causal effects evaluation in a/b testing with a reinforcement learning framework
A/B testing, or online experiment is a standard business strategy to compare a new product
with an old one in pharmaceutical, technological, and traditional industries. Major …
with an old one in pharmaceutical, technological, and traditional industries. Major …
Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic
Mediation analysis draws increasing attention in many research areas such as economics,
finance and social sciences. In this paper, we propose new statistical inference procedures …
finance and social sciences. In this paper, we propose new statistical inference procedures …
Integrative factor regression and its inference for multimodal data analysis
Multimodal data, where different types of data are collected from the same subjects, are fast
emerging in a large variety of scientific applications. Factor analysis is commonly used in …
emerging in a large variety of scientific applications. Factor analysis is commonly used in …
Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic
Mediation analysis draws increasing attention in many research areas such as economics,
finance and social sciences. In this paper, we propose new statistical inference procedures …
finance and social sciences. In this paper, we propose new statistical inference procedures …
Testing mediation effects using logic of boolean matrices
A central question in high-dimensional mediation analysis is to infer the significance of
individual mediators. The main challenge is that the total number of potential paths that go …
individual mediators. The main challenge is that the total number of potential paths that go …
Best-subset selection in generalized linear models: A fast and consistent algorithm via splicing technique
J Zhu, J Zhu, B Tang, X Chen, H Lin, X Wang - arXiv preprint arXiv …, 2023 - arxiv.org
In high-dimensional generalized linear models, it is crucial to identify a sparse model that
adequately accounts for response variation. Although the best subset section has been …
adequately accounts for response variation. Although the best subset section has been …
Fisher's combined probability test for high-dimensional covariance matrices
X Yu, D Li, L Xue - Journal of the American Statistical Association, 2024 - Taylor & Francis
Testing large covariance matrices is of fundamental importance in statistical analysis with
high-dimensional data. In the past decade, three types of test statistics have been studied in …
high-dimensional data. In the past decade, three types of test statistics have been studied in …