Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model

González–Manteiga, Wenceslao; Lombardía, María José; Molina, Isabel; Morales, Domingo; Santamaría, L.

doi:10.1016/j.csda.2006.01.012

Cited by 81 publications

(70 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of small-area estimation, the wild bootstrap is combined with bootstrap for finite populations to construct a finite artificial population replicating the real one [2]. For a small-area application, see [13]. This bootstrap estimator is robust to the lack of normality, and the procedure is as follows.…”

Section: Mean Squared Errormentioning

confidence: 99%

Adjusting economic estimates in business surveys

Ugarte

Militino

Goicoa

2008

Journal of Applied Statistics

View full text Add to dashboard Cite

Statistics for small areas within larger regions are recently required for many economic variables. However, when adding the estimates of the small areas within the larger regions, the results do not match up to those obtained with the appropriate estimator originally derived for the larger region. To avoid discrepancies between estimates benchmarking methods are commonly used in practice. In this paper, we discuss the suitability of using a restricted predictor versus a traditional direct calibrated estimator. The results are illustrated with the 2000 Business Survey of the Basque Country, Spain.benchmarking, restricted predictor, prorating estimator, linear mixed model, EBLUP, synthetic,

show abstract

Section: Mean Squared Errormentioning

confidence: 99%

Adjusting economic estimates in business surveys

Ugarte

Militino

Goicoa

2008

Journal of Applied Statistics

View full text Add to dashboard Cite

show abstract

“…They modified this towards a profiled likelihood to estimate simultaneously β and the variance components. González-Manteiga, Lombardía, Molina, Morales, and Santamaría (2007) also started with the penalised quasi-likelihood but estimated the variance components from a linearised version of the generalised linear model (cf. Schall 1991).…”

Section: Local Polynomial Estimation Of Semiparametric Mixed-effects mentioning

confidence: 99%

Local polynomial inference for small area statistics: estimation, validation and prediction

Sperlich

Lombardía

2010

Journal of Nonparametric Statistics

View full text Add to dashboard Cite

“…Pfeffermann and Tiller [17] proposed a parametric and a nonparametric bootstrap methods for estimating the same quantity under state-space models. Recently, Hall and Maiti [18,19] introduced a parametric and a matched-moment double-bootstrap algorithms, and González-Manteiga et al [20] applied a wild bootstrap procedure to logistic mixed models.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap mean squared error of a small-area EBLUP

González–Manteiga

Lombardía

Molina

et al. 2008

Journal of Statistical Computation and Simulation

Self Cite

113

View full text Add to dashboard Cite

Concerning the estimation of linear parameters in small areas, a nested-error regression model is assumed for the values of the target variable in the units of a finite population. Then, a bootstrap procedure is proposed for estimating the mean squared error (MSE) of the EBLUP under the finite population setup. The consistency of the bootstrap procedure is studied, and a simulation experiment is carried out in order to compare the performance of two different bootstrap estimators with the approximation given by Prasad and Rao [Prasad, N.G.N. and Rao, J.N.K., 1990, The estimation of the mean squared error of small-area estimators. Journal of the American Statistical Association, 85, 163-171.]. In the numerical results, one of the bootstrap estimators shows a better bias behavior than the Prasad-Rao approximation for some of the small areas and not much worse in any case. Further, it shows less MSE in situations of moderate heteroscedasticity and under mispecification of the error distribution as normal when the true distribution is logistic or Gumbel. The proposed bootstrap method can be applied to more general types of parameters (linear of not) and predictors.

show abstract

Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model

Cited by 81 publications

References 27 publications

Adjusting economic estimates in business surveys

Adjusting economic estimates in business surveys

Local polynomial inference for small area statistics: estimation, validation and prediction

Bootstrap mean squared error of a small-area EBLUP

Contact Info

Product

Resources

About