In many applications, identifying a single feature of interest requires
...
This paper proposes a new procedure to validate the multi-factor pricing...
In this paper, we develop a systematic theory for high dimensional analy...
Clustered effects are often encountered in multiple hypothesis testing o...
One common approach to detecting change-points is minimizing a cost func...
Testing the equality of two conditional distributions is at the core of ...
Generative, temporal network models play an important role in analyzing ...
Genomic data are subject to various sources of confounding, such as batc...
The proportional hazards model has been extensively used in many fields ...
Motivated by the increasing use of kernel-based metrics for high-dimensi...
Change-point detection has been a classical problem in statistics and
ec...
One fundamental statistical task in microbiome data analysis is differen...
Large-scale multiple testing is a fundamental problem in high dimensiona...
A common concern in observational studies focuses on properly evaluating...
The family-wise error rate (FWER) has been widely used in genome-wide
as...
Lasso is of fundamental importance in high-dimensional statistics and ha...
The paper presents new metrics to quantify and test for (i) the equality...
Conventional multiple testing procedures often assume hypotheses for
dif...
In this paper, we study distance covariance, Hilbert-Schmidt covariance ...
We consider high-dimensional inference for potentially misspecified Cox
...
Many statistical applications require the quantification of joint depend...