We develop a general approach to valid inference after model selection. At the core of our
framework is a result that characterizes the distribution of a post-selection estimator …

It is common practice in statistical data analysis to perform data-driven variable selection
and derive statistical inference from the resulting model. Such inference enjoys none of the …

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 …

Analysis of high-dimensional data often seeks to identify a subset of important features and
to assess the effects of these features on outcomes. Traditional statistical inference …

We develop a set of powerful and flexible size-correction procedures for general
nonstandard testing environments in which the asymptotic distribution of a test statistic is …

Quantile regression (QR) is a very useful statistical tool for learning the relationship between
the response variable and covariates. For many applications, one often needs to estimate …

We compare several confidence intervals after model selection in the setting recently
studied by Berk et al.[Ann. Statist. 41 (2013) 802–837], where the goal is to cover not the true …

We consider inference post-model-selection in linear regression. In this setting, Berk et
al.[Ann. Statist. 41 (2013a) 802–837] recently introduced a class of confidence sets, the so …

Valid inference after model selection is currently a very active area of research. The
polyhedral method, introduced in an article by Lee et al., allows for valid inference after …

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