About

Partha Lahiri is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He also serves as the Director of the Joint Program in Survey Methodology (JPSM) at the university. His professional work encompasses a broad range of topics in statistics, with a particular focus on small area estimation, Bayesian methods, empirical best prediction methods, probabilistic record linkage, and resampling methods. Professor Lahiri has contributed significantly to the field through his editorial roles, including serving as Guest Editor-in-Chief for a special issue on small area estimation in the Journal of the Royal Statistical Society, Series A, and editing volumes on Bayesian methods and model selection. His research addresses both theoretical and applied aspects of statistical science, including the development of new methodologies for variance estimation, confidence intervals, and prediction in complex models. He has also engaged in research related to COVID-19, such as estimating mask effectiveness perception for small domains using multiple data sources. Professor Lahiri's work is recognized for bridging Bayesian and classical approaches, advancing statistical analysis with linked data, and improving uncertainty measures in small area estimation problems.

Research topics

• Computer Science
• Artificial Intelligence
• Statistics
• Data Mining
• Machine Learning
• Mathematics
• Geography
• Engineering
• Remote sensing
• Economics
• Economic growth
• Econometrics

Selected publications

• Study on Star Formation History of Nearby Galaxies: A Bayesian Approach

  IAPQR TRANSACTIONS · 2026-04-01

  articleSenior author
  PublisherDOI

• Reproducibility package for Evaluating Alternative Approaches To Small Area Estimation Of Poverty With Survey And Census Data

  Reproducibility catalog · 2026-05-08

  otherOpen access
  PublisherDOI

• Multidimensional Poverty Mapping for Small Areas

  ArXiv.org · 2025-10-10

  preprintOpen accessSenior author

  Many countries measure poverty based only on income or consumption. However, there is a growing awareness of measuring poverty through multiple dimensions that captures a more reasonable status of poverty. Estimating poverty measure(s) for small geographical areas, commonly referred to as poverty mapping, is challenging due to small or no sample for the small areas. While there is a huge literature available on unidimensional poverty mapping, only a limited effort has been made to address special challenges that arise only in the multidimensional poverty mapping. For example, in multidimensional poverty mapping, a new problem arises involving estimation of relative contributions of different dimensions to overall poverty for small areas. This problem has been grossly ignored in the small area estimation (SAE) literature. We address this issue using a multivariate hierarchical model implemented via a Bayesian method. Moreover, we demonstrate how a multidimensional poverty composite measure can be estimated for small areas. In this paper, we demonstrate our proposed methodology using a survey data specially designed by one of us for multidimensional poverty mapping. This paper adds a new direction to poverty mapping literature.

  PublisherOA PDFDOI

• On the Hierarchical Bayes justification of Empirical Bayes Confidence Intervals

  ArXiv.org · 2025-11-17

  preprintOpen accessSenior author

  Multi-level normal hierarchical models, also interpreted as mixed effects models, play an important role in developing statistical theory in multi-parameter estimation for a wide range of applications. In this article, we propose a novel reconciliation framework of the empirical Bayes (EB) and hierarchical Bayes approaches for interval estimation of random effects under a two-level normal model. Our framework shows that a second-order efficient empirical Bayes confidence interval, with EB coverage error of order $O(m^{-3/2})$, $m$ being the number of areas in the area-level model, can also be viewed as a credible interval whose posterior coverage is close to the nominal level, provided a carefully chosen prior - referred to as a 'matching prior' - is placed on the hyperparameters. While existing literature has examined matching priors that reconcile frequentist and Bayesian inference in various settings, this paper is the first to study matching priors with the goal of interval estimation of random effects in a two-level model. We obtain an area-dependent matching prior on the variance component that achieves a proper posterior under mild regularity conditions. The theoretical results in the paper are corroborated through a Monte Carlo simulation study and a real data analysis.

  PublisherOA PDFDOI

• Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning

  Oxford Bulletin of Economics and Statistics · 2025-04-14 · 1 citations

  articleOpen accessSenior author

  ABSTRACT Estimates of poverty are an important input into policy formulation in developing countries, making the accurate measurement of poverty rates a first‐order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the accuracy and precision of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that empirical best prediction (EBP) based on a twofold household‐level model outperforms EBPs based on other common small area estimation models. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve poverty estimates in developing countries, even for small subgroups.

  PublisherOA PDFDOI

• Empirical best prediction of poverty indicators via nested error regression with high dimensional parameters

  ArXiv.org · 2025-02-21

  preprintOpen access

  The Nested Error Regression Model with High-Dimensional Parameters (NERHDP) is extended to address challenges in small area poverty estimation. A robust and flexible framework is proposed to derive empirical best predictors (EBPs) of small area poverty indicators while accommodating heterogeneity in regression coefficients and sampling variances across areas. To mitigate the computational limitations of the existing algorithm, an efficient estimation procedure is introduced, substantially reducing computation time and enhancing scalability for large datasets. A novel approach for generating area-specific poverty estimates in out-of-sample areas is also developed, improving the reliability of synthetic estimates. Uncertainty is quantified through a parametric bootstrap method specifically tailored to the extended model. Under heterogeneous data-generating scenarios, the proposed method yields lower relative bias and relative root mean squared prediction error than existing approaches. The methodology is further illustrated using data from the 2002 Albania Living Standards Measurement Survey, combined with auxiliary information from the 2001 census, to estimate poverty indicators for 374 municipalities.

  PublisherOA PDFDOI

• Impact of Existence and Nonexistence of Pivot on the Coverage of Empirical Best Linear Prediction Intervals for Small Areas

  Journal of the American Statistical Association · 2025-11-05

  articleSenior author
  PublisherDOI

• Special issue SMA: big data and alternative data sources for small area estimation

  Statistical Methods & Applications · 2024-09-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Effects of model misspecification on small area estimators

  arXiv (Cornell University) · 2024-03-17 · 1 citations

  preprintOpen access

  Nested error regression models are commonly used to incorporate observational unit specific auxiliary variables to improve small area estimates. When the mean structure of this model is misspecified, there is generally an increase in the mean square prediction error (MSPE) of Empirical Best Linear Unbiased Predictors (EBLUP). Observed Best Prediction (OBP) method has been proposed with the intent to improve on the MSPE over EBLUP. We conduct a Monte Carlo simulation experiment to understand the effect of mispsecification of mean structures on different small area estimators. Our simulation results lead to an unexpected result that OBP may perform very poorly when observational unit level auxiliary variables are used and that OBP can be improved significantly when population means of those auxiliary variables (area level auxiliary variables) are used in the nested error regression model or when a corresponding area level model is used. Our simulation also indicates that the MSPE of OBP in an increasing function of the difference between the sample and population means of the auxiliary variables.

  PublisherOA PDFDOI

• Improving measurement error and representativeness in nonprobability surveys

  arXiv (Cornell University) · 2024-10-23 · 1 citations

  preprintOpen accessSenior author

  In the age of big data, nonprobability surveys are becoming increasingly abundant. Data integration techniques involving both probability and nonprobability surveys are being extensively used for providing improved estimates for finite population estimation. While much of the existing research has focused on mitigating selection bias in nonprobability surveys, the issue of measurement error within these surveys remains relatively unexplored. Statistical methods devised with the purpose of reducing selection bias are appropriate for reliable estimation, only under the assumption of accuracy of survey responses. Motivated by a recent case study of Kennedy, Mercer, and Lau (2024), our research addresses bias from both measurement and sampling errors in nonprobability surveys. In this article, we propose a new data integration method that uses multiple probability and nonprobability surveys and leverages machine learning models to construct a composite estimator. The proposed composite estimator integrates probability and nonprobability surveys, when both contain response variables of interest. We analyze the performance of this estimator in comparison to an existing composite estimator in literature, analytically as well as empirically, using multiple survey data from Kennedy et al. (2024). Finally, we identify conditions under which the proposed estimator outperforms estimators based solely on probability surveys.

  PublisherOA PDFDOI

Recent grants

• On Area Specific Uncertainty Measures in Small Area Estimation

  NSF · $220k · 2015–2019

• Collaborative Research: Computation-driven small area inference with applications

  NSF · $98k · 2009–2013

• Statistical Analysis with Computerized Linked Data

  NSF · $300k · 2018–2022

Frequent coauthors

• Jiming Jiang

  University of California, Davis

  18 shared

• Hermann Habermann

  University of Maryland, College Park

  16 shared

• Courtney Kennedy

  McMaster University

  16 shared

• B.N. Biswas

  12 shared

• Santanu Pramanik

  Krea University

  11 shared

• Debasish Mondal

  11 shared

• Shu‐Mei Wan

  Lunghwa University of Science and Technology

  9 shared

• Michael Larsen

  Saint Michael's College

  9 shared