Robust Empirical Best Small Area Finite Population Mean Estimation Using a Mixture Model

Autor:	Julie Gershunskaya, Partha Lahiri
Rok vydání:	2017
Předmět:	Estimation education.field_of_study Population mean 05 social sciences Population General Medicine Mixture model 01 natural sciences 010104 statistics & probability 0502 economics and business Outlier Statistics 0101 mathematics education 050205 econometrics Mathematics
Zdroj:	Calcutta Statistical Association Bulletin. 69:183-204
ISSN:	2456-6462 0008-0683
DOI:	10.1177/0008068317722297
Popis:	We propose a new robust empirical best estimation approach to estimate small area finite population means that are relatively insensitive to a model misspecification or to the presence of outliers. This important robustness property is achieved by replacing the standard normality assumption of the sampling errors in a nested-error regression (NER) model by a scale mixture of two normal distributions with different variances. We present a formal statistical test to identify if a small area is an outlier and provide an efficient new computing algorithm to implement our procedure. We examine the finite sample robustness properties of our proposed method using a Monte Carlo simulation and compare the proposed method with alternative existing methods in a study using data from the Current Employment Statistics (CES) survey conducted by the US Bureau of Labor Statistics (BLS).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::df6a2fc77c445294e4e39df3faa3301d https://doi.org/10.1177/0008068317722297 Zobrazit plný text záznamu