Investigating the Performance of Horvitz-Thompson and Generalized Regression Estimators in Small Domains with Application to Income and Expenditure of Households in Makurdi-Nigeria

A J Ikughur,M T Imande,S Vambe

doi:10.46912/napas.124

Abstract

In this study, efficiencies of Horvitz-Thompson (HT) and Generalized Regression (GREG) estimatorsare investigated in small domains namely Domain 1, 2 and 3. Efficiency criteria include the standarderror of the estimates (SE) and coefficient of variation (CV) Computed for the study population ofsize 1052 households consisting of domain size of 885, 151 and 16 and domain samples sizes of 167,29 and 3 respectively for Domains 1, 2 and 3. The estimates produced by HT estimator GREGestimator performs better than HT estimator with domain

Highlights

Small-Area Estimation (SAE) is a collection of statistical techniques designed for improving sample survey estimates through the use of auxiliary information
SAE is of great importance in survey sampling due to a growing demand for reliable small area statistics from both public and private sectors for several reasons such as effective planning of health, mobilization and apportionment of resources and revenue and allocation of legislative seats as the case may be (Ghosh and Rao, 1994; Rao, 2003)
The domain sizes and their respective sample sizes proportional to their domain sizes are given in Table 1.Table 2 below shows the total and aveage expenditure of persons in the three small domains in Makurdi using the Renaissance classifications of 2011when information about their incomes are available

Summary

Introduction

Small-Area Estimation (SAE) is a collection of statistical techniques designed for improving sample survey estimates through the use of auxiliary information. SAE is of great importance in survey sampling due to a growing demand for reliable small area statistics from both public and private sectors for several reasons such as effective planning of health, mobilization and apportionment of resources and revenue and allocation of legislative seats as the case may be (Ghosh and Rao, 1994; Rao, 2003). There are several estimators that can be used in SAE when auxiliary variables are available. These include Hansen-Hurwitz estimator (HH), RaoHartley-Cochran estimator, Horvitz-Thompson (HT) estimator, Regression Estimator, ModelDependent Empirical Best Linear Unbiased Predictor (EBLUP), synthetic, generalised regression (GREG) estimator etc

Methods

Discussion

Conclusion