Post-selection inference
AK Kuchibhotla, JE Kolassa… - Annual Review of …, 2022 - annualreviews.org
We discuss inference after data exploration, with a particular focus on inference after model
or variable selection. We review three popular approaches to this problem: sample splitting …
or variable selection. We review three popular approaches to this problem: sample splitting …
On selection and conditioning in multiple testing and selective inference
We investigate a class of methods for selective inference that condition on a selection event.
Such methods follow a two-stage process. First, a data-driven collection of hypotheses is …
Such methods follow a two-stage process. First, a data-driven collection of hypotheses is …
Conditional versus unconditional approaches to selective inference
We investigate a class of methods for selective inference that condition on a selection event.
Such methods operate in a two-stage process. First, a (sub) collection of hypotheses is …
Such methods operate in a two-stage process. First, a (sub) collection of hypotheses is …
Post-Selection Inference for the Cox Model with Interval-Censored Data
We develop a post-selection inference method for the Cox proportional hazards model with
interval-censored data, which provides asymptotically valid p-values and confidence …
interval-censored data, which provides asymptotically valid p-values and confidence …
Bayesian selective inference
DG Rasines, GA Young - Handbook of Statistics, 2022 - Elsevier
We discuss Bayesian inference for parameters selected using the data. First, we provide a
critical analysis of the existing positions in the literature regarding the correct Bayesian …
critical analysis of the existing positions in the literature regarding the correct Bayesian …
[PDF][PDF] Building a foundation in statistics in the era of data science
AL Gibbs - Proceedings of the Tenth International Conference on …, 2018 - iase-web.org
In the era of data science, our undergraduate statistics programs need to cover more
territory, including data analysis, statistical theory, computational skills for data wrangling …
territory, including data analysis, statistical theory, computational skills for data wrangling …
Bayesian Selective Inference: Non-informative Priors
DG Rasines, GA Young - arXiv preprint arXiv:2008.04584, 2020 - arxiv.org
We discuss Bayesian inference for parameters selected using the data. First, we provide a
critical analysis of the existing positions in the literature regarding the correct Bayesian …
critical analysis of the existing positions in the literature regarding the correct Bayesian …
[PDF][PDF] Empirical model assessment and uncertainty quantification
P Jonathan - 2020 - ygraigarw.github.io
The objective of this document is to provide a concise introduction to considerations for
empirical assessment of predictive models. This includes an overview of existing relevant …
empirical assessment of predictive models. This includes an overview of existing relevant …