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Small area estimation for semicontinuous data

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Title Small area estimation for semicontinuous data
Not Available
 
Creator Hukum Chandra
Ray Chambers
 
Subject Mean squared error
Parametric bootstrap
Skewed data
Small area estimation
Zero-inflated
 
Description Not Available
Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either
take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on
the positive real line. Standard methods for small area estimation (SAE) based on the use of linearmixed
models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables
under a two part random effects model that allows for the presence of excess zeros as well as the skewed
nature of the nonzero values of the response variable. In particular, we first model the excess zeros via
a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being
observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted
on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small
area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method
to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are
compared to the true MSE in a simulation study.
Not Available
 
Date 2018-10-27T10:00:16Z
2018-10-27T10:00:16Z
2014-01-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/8351
 
Language English
 
Relation Not Available;
 
Publisher WILEY-VCH Verlag GmbH & Co. KGaA