Andrew Gelman

Andrew Gelman

Columbia University in the City of New York

H-index: 129

North America-United States

About Andrew Gelman

Andrew Gelman, With an exceptional h-index of 129 and a recent h-index of 91 (since 2020), a distinguished researcher at Columbia University in the City of New York, specializes in the field of statistics, political science.

His recent articles reflect a diverse array of research interests and contributions to the field:

Model validation for aggregate inferences in out-of-sample prediction

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models

Past, Present and Future of Software for Bayesian Inference

Active Statistics: Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference

Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes

Commentaries on “Beyond statistical significance: Five principles for the new era of data analysis and reporting”

Using leave‐one‐out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale

Regression, poststratification, and small-area estimation with sampling weights

Andrew Gelman Information

University

Position

Professor of Statistics and Political Science

Citations(all)

180737

Citations(since 2020)

87586

Cited By

129029

hIndex(all)

129

hIndex(since 2020)

91

i10Index(all)

336

i10Index(since 2020)

258

Email

University Profile Page

Columbia University in the City of New York

Google Scholar

View Google Scholar Profile

Andrew Gelman Skills & Research Interests

statistics

political science

Top articles of Andrew Gelman

Title

Journal

Author(s)

Publication Date

Model validation for aggregate inferences in out-of-sample prediction

Lauren Kennedy

Aki Vehtari

Andrew Gelman

2024/2/15

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models

medRxiv

Judith Bouman

Anthony Hauser

Simon L Grimm

Martin Wohlfender

Samir Bhatt

...

2023

Past, Present and Future of Software for Bayesian Inference

Erik Štrumbelj

Alexandre Bouchard-Côté

Jukka Corander

Andrew Gelman

Håvard Rue

...

2024/2

Active Statistics: Stories, Games, Problems, and Hands-on Demonstrations for Applied Regression and Causal Inference

Andrew Gelman

Aki Vehtari

2024/3/31

Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes

arXiv preprint arXiv:2403.00639

Jonas Mikhaeil

Andrew Gelman

Philip Greengard

2024/3/1

Commentaries on “Beyond statistical significance: Five principles for the new era of data analysis and reporting”

Journal of Consumer Psychology

Norbert Schwarz

Fritz Strack

Andrew Gelman

Stijn van Osselaer

Joel Huber

2023

Using leave‐one‐out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale

Statistics in Medicine

Swen Kuh

Lauren Kennedy

Qixuan Chen

Andrew Gelman

2024/2/28

Regression, poststratification, and small-area estimation with sampling weights

Andrew Gelman

Yajuan Si

Brady T West

2024/2/19

Prediction scoring of data-driven discoveries for reproducible research

Statistics and Computing

Anna L Smith

Tian Zheng

Andrew Gelman

2023/2

BISG: When inferring race or ethnicity, does it matter that people often live near their relatives?

arXiv preprint arXiv:2304.09126

Philip Greengard

Andrew Gelman

2023/4/18

For how many iterations should we run Markov chain Monte Carlo?

arXiv preprint arXiv:2311.02726

Charles C Margossian

Andrew Gelman

Alexandre Dumas

2023/11/5

Who Wants School Vouchers in America? A Comprehensive Study Using Multilevel Regression and Poststratification

Social Sciences

Yu-Sung Su

Andrew Gelman

2023/7/31

In Pursuit of Campus-Wide Data Literacy: A Guide to Developing a Statistics Course for Students in Nonquantitative Fields

Journal of Statistics and Data Science Education

Alexis Lerner

Andrew Gelman

2023/12/12

“Two truths and a lie” as a class-participation activity

The American Statistician

Andrew Gelman

2023/1/2

An improved BISG for inferring race from surname and geolocation

arXiv preprint arXiv:2304.09126

Philip Greengard

Andrew Gelman

2023/4

Correcting Measurement Error Bias in Conjoint Survey Experiments

Katherine Clayton

Yusaku Horiuchi

Aaron R Kaufman

Gary King

Mayya Komisarchik

2023/7

Scoring multilevel regression and postratification based population and subpopulation estimates

arXiv preprint arXiv:2312.06334

Lauren Kennedy

Aki Vehtari

Andrew Gelman

2023/12/11

Challenges in adjusting a survey that overrepresents people interested in politics

Harvard Data Science Review

Andrew Gelman

Gustavo Novoa

2023/9/12

Estimating Time-Dependent Transmission Rates of Sars-Cov-2 in a Stratified Population: A Systematic Comparison of Three Methods

9TH INTERNATIONAL CONFERENCE ON INFECTIOUS DISEASE DYNAMICS P

Judith Bouman

Anthony Hauser

Simon Grimm

Martin Wohlfender

Christian Althaus

...

2023

Inference from nonrandom samples using Bayesian machine learning

Journal of Survey Statistics and Methodology

Yutao Liu

Andrew Gelman

Qixuan Chen

2023/4/1

See List of Professors in Andrew Gelman University(Columbia University in the City of New York)