Small area estimation of additive parameters under unit-level generalized linear mixed models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F20%3A00336508" target="_blank" >RIV/68407700:21340/20:00336508 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.2436/20.8080.02.93" target="_blank" >https://doi.org/10.2436/20.8080.02.93</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2436/20.8080.02.93" target="_blank" >10.2436/20.8080.02.93</a>

Result language

angličtina

Original language name

Small area estimation of additive parameters under unit-level generalized linear mixed models

Original language description

Average incomes and poverty proportions are additive parameters obtained as averages of a given function of an income variable. As the variable income has an asymmetric distribution, it is not properly modeled via normal distributions. When dealing with this type of variables, a first option is to apply transformations that approach normality. A second option is to use nonsymmetric distributions from the exponential family. This paper proposes unit-level generalized linear mixed models for modeling asymmetric positive variables and for deriving three types of predictors of small area additive parameters, called empirical best, marginal and plug-in. The parameters of the introduced model are estimated by applying the maximum likelihood method to the Laplace approximation of the likelihood. The mean squared errors of the predictors are estimated by parametric bootstrap. The introduced methodology is applied and illustrated under unit-level gamma mixed models. Some simulation experiments are carried out to study the behavior of the fitting algorithm, the small area predictors and the bootstrap estimator of the mean squared errors. By using data of the Spanish living condition survey of 2013, an application to the estimation of average incomes and poverty proportions in counties of the region of Valencia is given.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/EF16_019%2F0000778" target="_blank" >EF16_019/0000778: Center for advanced applied science</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SORT

ISSN

1696-2281

e-ISSN

2013-8830

Volume of the periodical

44

Issue of the periodical within the volume

1

Country of publishing house

ES - SPAIN

Number of pages

36

Pages from-to

3-38

UT code for WoS article

000544353100001

EID of the result in the Scopus database

2-s2.0-85090207684