Mark Handcock

Distinguished Professor of Statistics

University of California, Los Angeles

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Mark Handcock is a Distinguished Professor of Statistics at the University of California, Los Angeles, United States. His research focuses on statistical modeling, particularly in areas such as evolutionary games, network analysis, and causal inference. He has published recent articles exploring topics including Antarctic sea ice dynamics and the transmission patterns of COVID-19 in Mongolia.

Articles (18)

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

UNITED STATES

View details

Narayan Sastry

Research Professor

University of Michigan

UNITED STATES

View details

Akihiro Nishi

University of California, Los Angeles

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Mark Handcock

Research Interests

Explore related searches

Contact this professor

About

Articles (18)

A twenty-first century structural change in Antarctica’s sea ice system

Statistical modelling of networked evolutionary public goods games

Modeling the visibility distribution for respondent-driven sampling with application to population size estimation

Causal inference over stochastic networks

A Bayesian Model for 20th Century Antarctic Sea Ice Extent Reconstruction

Estimating Asymptomatic and Symptomatic Transmission of the COVID‐19 First Few Cases in Selenge Province, Mongolia

20th Century Antarctic sea ice extent anomaly reconstruction by sector

Comparative assessment of alternative methods for evaluating the optimality of ground motion intensity measures using woodframe buildings

Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting

Understanding networks with exponential-family random network models

Modeling of networked populations when data is sampled or missing

A practical revealed preference model for separating preferences and availability effects in marriage formation

Practical Network Modeling via Tapered Exponential-family Random Graph Models

A regime shift in seasonal total Antarctic sea ice extent in the twentieth century

A new record minimum for Antarctic sea ice

Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis

A note on “sequential neighborhood effects” by hicks et al. (2018)

Eighteen-year record of circum-Antarctic landfast-sea-ice distribution allows detailed baseline characterisation and reveals trends and variability

Collaborators (9)

Heide Jackson

Christopher Longhurst

Ryan Fogt

Duncan Clark

Thomas Maierhofer

Camille Nebeker

Eliah S. Aronoff-Spencer

Narayan Sastry

Akihiro Nishi

Make your next move.