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Small area estimation of proportions with different levels of auxiliary data

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Title Small area estimation of proportions with different levels of auxiliary data
Not Available
 
Creator Hukum Chandra
Sushil Kumar
Kaustav Aditya
 
Subject auxiliary data
binary data
empirical best predictor
generalized linear mixed model
indirect estimator
 
Description Not Available
Binary data are often of interest in many small areas of applications. The use of standard
small area estimation methods based on linear mixed models becomes problematic
for such data. An empirical plug-in predictor (EPP) under a unit-level generalized
linear mixed model with logit link function is often used for the estimation of a small
area proportion. However, this EPP requires the availability of unit-level population
information for auxiliary data that may not be always accessible. As a consequence,
in many practical situations, this EPP approach cannot be applied. Based on the level
of auxiliary information available, different small area predictors for estimation of
proportions are proposed. Analytic and bootstrap approaches to estimating the mean
squared error of the proposed small area predictors are also developed. Monte Carlo
simulations based on both simulated and real data show that the proposed small area
predictors work well for generating the small area estimates of proportions and represent
a practical alternative to the above approach. The developed predictor is applied
to generate estimates of the proportions of indebted farm households at district-level
using debt investment survey data from India.
Not Available
 
Date 2018-10-18T14:28:52Z
2018-10-18T14:28:52Z
2018-01-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/7999
 
Language English
 
Relation Not Available;
 
Publisher WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim