activity
20072023
most citedParametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models

123 citations · 167 across the 11 of their papers we have counts for

collaborators

12 papers

stat.ME2023

Estimation of finite population proportions for small areas -- a statistical data integration approach

Aditi Sen, Partha Lahiri

Empirical best prediction (EBP) is a well-known method for producing reliable proportion estimates when the primary data source provides only small or no sample from finite populat…

stat.ME2022★ 1 cited

Approximate hierarchical Bayes small area estimation using NEF-QVF and poststratification

Soumojit Das, Partha Lahiri

We propose an approximate hierarchical Bayes approach that uses the Natural Exponential Family with Quadratic Variance Function in combining information from multiple sources to im…

stat.ME2022

A Nested Error Regression Model with High Dimensional Parameter for Small Area Estimation

Partha Lahiri, Nicola Salvati

In this paper we propose a flexible nested error regression small area model with high dimensional parameter that incorporates heterogeneity in regression coefficients and variance…

stat.AP2021

Estimation of mask effectiveness perception for small domains using multiple data sources

Aditi Sen, Partha Lahiri

All pandemics are local; so learning about the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting ever…

stat.ME2020★ 1 cited

Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model

Takumi Saegusa, Shonosuke Sugasawa, Partha Lahiri

The multivariate Fay-Herriot model is quite effective in combining information through correlations among small area survey estimates of related variables or historical survey esti…

stat.ME2019

Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models

Masayo Y. Hirose, Partha Lahiri

The two-level normal hierarchical model has played an important role in statistical theory and applications. In this paper, we first introduce a general adjusted maximum likelihood…

stat.AP2018

A General Bayesian Approach to Meet Different Inferential Goals in Poverty Research for Small Areas

Partha Lahiri, Jiraphan Suntornchost

Poverty mapping that displays spatial distribution of various poverty indices is most useful to policymakers and researchers when they are disaggregated into small geographic units…

stat.ME2018

An Evaluation of Design-based Properties of Different Composite Estimators

Daniel Bonnéry, Yang Cheng, Partha Lahiri

For the last several decades, the US Census Bureau has been using the AK composite estimation method to produce statistics on employment from the Current Population Survey (CPS) da…

math.ST2017

A New Model Variance Estimator for an Area Level Small Area Model to Solve Multiple Problems Simultaneously

Masayo Yoshimori Hirose, Partha Lahiri

The two-level normal hierarchical model (NHM) has played a critical role in the theory of small area estimation (SAE), one of the growing areas in statistics with numerous applicat…

math.ST2014★ 30 cited

A second-order efficient empirical Bayes confidence interval

Masayo Yoshimori, Partha Lahiri

We introduce a new adjusted residual maximum likelihood method (REML) in the context of producing an empirical Bayes (EB) confidence interval for a normal mean, a problem of great…

math.ST2008★ 123 cited

Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models

Snigdhansu Chatterjee, Partha Lahiri, Huilin Li

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly usefu…

math.ST2007★ 12 cited

On the design-consistency property of hierarchical Bayes estimators in finite population sampling

P. Lahiri, Kanchan Mukherjee

We obtain a limit of a hierarchical Bayes estimator of a finite population mean when the sample size is large. The limit is in the sense of ordinary calculus, where the sample obse…