000			02120nam a2200337 i 4500
001			500000901
003			UVAL
005			20240507115312.0
007			ta
008			170718s2017 chl g 000 0 spa d
040			_aDIBRA _bspa _cUVAL _erda
041	0		_aspa
084			_aM
100	0		_aÁvila Valdez, José Luis, _eauthor.
245	0	0	_aUsingthe EM algorithm in small area estimation whith the Fay-Herriot model / _cJosé Luis Ávila Valdez.
264		1	_aValparaíso : _bUniversidad de Valparaíso, _c2017.
300			_a123 hojas.
500			_aCONICYT - PCHA/Doctorado Nacional/2014-63140019.
502			_aDoctorado en Estadística.
520			_aStandard methods of variance component estimation used in the Fay-Herriot model for small area estimation can produce inadmissible (negative or zero) values of these variances. This implies that the empirical best linear unbiased predictor of a small area mean does not take into account the variance of the random effect of small areas, reducing it only to a regression estimator. In this work, we consider an approach based on the expectation-maximization (EM) algorithm to solve this problem of inadmissibility in both univariate and multivariate Fay-Herriot model. We show through Monte Carlo simulations that the EM algorithm always produces strictly positive model variance component estimates. In the univariate case, we compare the performance of the proposed approach for two recent methods in terms of relative bias, mean square error and mean square predictor error. We apply our approach to a food security and poverty data set collected in Mexico.
650		0	_aALGORITMOS.
650		0	_aALGORITMOS DE EXPECTACION - MAXIMIZACION.
650		0	_aMAXIMOS Y MINIMOS.
700	1		_aLeiva Sánchez, Víctor E., _eProfesor guía _948320.
700	1		_aRiquelme, Marco, _eProfesor guía _998458.
710	2		_aUniversidad de Valparaíso (Chile). _bFacultad de Ciencias. _bInstituto de Estadística _9228632.
942			_c5 _2ddc
999			_c91093 _d91093