Professor, Statistics Program
Department of Mathematics
Professor and Director
Joint Program in Survey Methodology (JPSM)
University of Maryland
College Park, MD 20742
- Office: Lefrak 1218
- Telephone: 301 314 5903
- Fax: 301-314-7912
- Email (the best way to reach me):
Basic Information on the RIT topic
SiT Special Issue 2020
IMS Monograph on Model Selection (ed. P.Lahiri)
RIT SAE Sessions
Next RIT Presentation
May 11, 3:30-4:30 UN Toolkit on small area estimation for the Sustainable Development Goals
Haoyi Chen, Coordinator, Inter-Secretariat Working Group on Household Surveys, United Nations
Yongyi Min, Chief, Sustainable Development Goal Monitoring Section, UN Statistics Division, United Nations
William Bell (U.S. Census Bureau), Carolina Casas-Cordero (Universidad Católica de Chile), Carolina Franco (U.S. Census Bureau), Graham Kalton (JPSM), Benmei Liu (U.S. National Cancer Institute), Monica Pratesi (University of Pisa, Italy), J.N.K. Rao (Carleton University, Canada)
Previous RIT Presentations
Feb 2 and Feb 9: An introduction to small srea estimation (Speaker: Partha Lahiri, University of Maryland College Park)
Feb 16: Parametric bootstrap methods in small area estimation (Speaker: Snigdhansu Chatterjee, University of Minnesota)
Feb 23: Data fusion for solving small area estimation (Speaker: Ben Kedem, University of Maryland College Park)
March 2: SAE Projects in the World Bank (Speaker: David Newhouse, World Bank Group)
March 9: SAE in Transportation (Speaker: Cinzia Cirillo, University of Maryland College Park)
March 23: SAE method for employee compensation (Speaker: Andreea Erciulescu, Westat)
March 30: Small area estimation in the presence of
measurement error in the covariates (Speaker: Carolina Franco, Census Bureau)
April 6: Parametric bootstrap for SAE (Speaker: Takumi Saegusa, University of Maryland College Park)
3:30-4 PM: Parametric Bootstrap for SAE (Speaker: Sheyda Peyman, AMSC-UMD student)
Paper to discuss
4-4:30 PM: Adjusted Maximum Likelihood methods for SAE (Speaker: Phillip Koshute, AMSC-UMD student)
Paper to discuss
April 20: Hybrid BRR and Parametric-Model Variance Estimates for Small Domains in Large Surveys (Speaker: Eric Slud, University of Maryland College Park)
April 27: Post-election Analysis of Presidential Election/Poll Data:
Liars Continued to Lie But What Have Changed? (Speaker: Jiming Jiang, University of California, Davis)
May 4: Implementation of model-based estimates in support
of the USDA NASS crops county estimates program (Speaker: Nathan Cruze and Lu Chen, NASS/USDA)
Cruze et al. (2019)
Erciulescu et a. (2018)
COVID-19 Related Papers
Sen, A. and Lahiri, P. (2020), Estimation of mask effectiveness perception for small domains using multiple data sources, unpublished manuscript.
Hirose, M. and Lahiri, P. (2020), Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models , Journal of the American Statistical Association.
Ganesh, N. and Lahiri, P. (2008), A new class of average moment matching priors , Biometrika.
Probabilistic Record Linkage
Han, M. and Lahiri, P. (2018), Statistical Analysis with Linked Data , International Statistical Review.
Lahiri, P. and Larsen, M. (2005), Regression analysis with linked data , Journal of the American Statistical Association.
Empirical Best Prediction Methods
Hirose, M. and Lahiri, P. (2018),Estimating variance of random effects to solve multiple problems simultaneously , Annals of Statistics.
Yoshimori, M. and Lahiri, P. (2014),A second-order efficient empirical Bayes confidence interval , Annals of Statistics.
Yoshimori, M. and Lahiri, P. (2014),A new adjusted maximum likelihood method for the Fay-Herriot small area model , Journal of Multivariate Analysis.
Jiang, J. and Lahiri, P. (2006),Mixed model prediction and small area estimation, Editor's invited discussion paper , Test.
Datta, G. and Lahiri, P. (2000),A unified measure of uncertainty of estimated best linear unbiased predictors in small area estimation problems , Statistica Sinica.
Jiang, J, Lahiri, P. and Nguyen, T. (2018),A Unified Monte-Carlo Jackknife for Small Area
Estimation after Model Selection , Annals of Mathematical Sciences and Applications.
Chatterjee, S., Lahiri, P. and Li, H. (2008), On small area prediction interval problems , Annals of Statistics.
Jiang, J, Lahiri, P. and Wan, S. (2003),Jackknifing the mean squared error of empirical best predictor , Annals of Statistics.
Butar, F. and Lahiri, P. (2003),On the measures of uncertainty of empirical Bayes small-area estimators , Journal of Statistical Planning and Inference.
© Partha Lahiri, December 14, 2020.