Current attempts at methodological reform in sciences come in response to an overall lack of
rigor in methodological and scientific practices in experimental sciences. However, most …

We propose new inference tools for forward stepwise regression, least angle regression,
and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a …

Inspired by sample splitting and the reusable holdout introduced in the field of differential
privacy, we consider selective inference with a randomized response. We discuss two major …

Conditional selective inference (SI) has been studied intensively as a new statistical
inference framework for data-driven hypotheses. The basic concept of conditional SI is to …

Principal component analysis (PCA) is a well-known tool in multivariate statistics. One
significant challenge in using PCA is the choice of the number of principal components. In …

While many methods are available to detect structural changes in a time series, few
procedures are available to quantify the uncertainty of these estimates post-detection. In this …

Changepoint detection methods are used in many areas of science and engineering, for
example, in the analysis of copy number variation data to detect abnormalities in copy …

Investigators often use the data to generate interesting hypotheses and then perform
inference for the generated hypotheses. P-values and confidence intervals must account for …

Multiple testing problems arising in modern scientific applications can involve
simultaneously testing thousands or even millions of hypotheses, with relatively few true …

This paper provides an alternative approach to penalized regression for model selection in
the context of high‐dimensional linear regressions where the number of covariates is large …