Numerical Issues in Statistical Computing for the Social Scientist

Citation:

Gill, Jeff, Micah Altman, and Michael McDonald. Numerical Issues in Statistical Computing for the Social Scientist, 2003.

Notes:

Reviews:

"A compact guide to the voluminous literature on optimisation, numerical analysis, and computational statistics. This is no small achievement." - Statistical Software Newsletter in Computational Statistics and Data Analysis.

"…an excellent text. It has the potential to be enormously influential across the social sciences…It should be required reading for everyone who performs statistical computing at the advanced level…" - Journal of the American Statistical Association, June 2005

Other reviews: Technometrics Review, ACM Review, SciTech Book News Review, Choice Review, Computational Statistics and Data Analysis Review, Journal of Statistical Software Review, Statistical Methods in Medical Research Review, Journal of Statistical Computation and Simulation Review, Corrigenda.

John Wiley & Sons

Last updated on 06/04/2020

Contact

American University
(202) 885-6275
4400 Massachusetts Avenue, NW
Washington, DC 20016
jgill@american.edu

Publications by Type:

Recent Publications

Gill, Jeff. “Political Science Is A Data Science.” Journal of Politics (Forthcoming).

jop_address.pdf

Gill, Jeff, and Kumail Wasif. “Bayesian Analyses of Political Decision Making.” In Oxford Encyclopedia of Political Decision Making, edited by Dave Redlawsk, Forthcoming. Publisher's Version

Gill, Jeff. “Measuring Constituency Ideology Using Bayesian Universal Kriging.” State Politics & Policy Quarterly (2020). Abstract

sppq2020.pdf

sppqappendices.pdf

In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state,
congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

JEFF GILL

Distinguished Professor,

Department of Government
Department of Mathematics & Statistics,

Founding Director, Center for Data Science
Member, Center for Behavioral Neuroscience

Numerical Issues in Statistical Computing for the Social Scientist

Citation:

Notes:

Contact

Publications by Type:

Recent Publications

564f00644b4c4c0124c207bc10176a44

JEFF GILL

Distinguished Professor,

Department of GovernmentDepartment of Mathematics & Statistics,

Founding Director, Center for Data ScienceMember, Center for Behavioral Neuroscience

Citation:

Notes:

Contact

Publications by Type:

Recent Publications

564f00644b4c4c0124c207bc10176a44

Department of Government
Department of Mathematics & Statistics,

Founding Director, Center for Data Science
Member, Center for Behavioral Neuroscience