Parametric Bootstrap Procedures for Small Area Prediction Variance
نویسندگان
چکیده
A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a unit level model. It is demonstrated that the proposed procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples. Applications to a logit model under different types of auxiliary information are discussed.
منابع مشابه
Comparing two testing procedures in unbalanced two-way ANOVA models under heteroscedasticity: Approximate degree of freedom and parametric bootstrap approach
The classic F-test is usually used for testing the effects of factors in homoscedastic two-way ANOVA models. However, the assumption of equal cell variances is usually violated in practice. In recent years, several test procedures have been proposed for testing the effects of factors. In this paper, the two methods that are approximate degree of freedom (ADF) and parametric bootstr...
متن کاملParametric bootstrap methods for bias correction in linear mixed models
The empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To obtain a second-order unbiased estimator of the MSE, the second-order bias correction has been derived mainly based on Taylor series expansions. However, thi...
متن کاملAn adjusted maximum likelihood method for solving small area estimation problems
For the well-known Fay–Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in small area estimation, could reduce to a simple regression estimator, which typically has an overshrinking problem. We propos...
متن کاملMultivariate Fay-Herriot models for small area estimation
Introduction Multivariate Fay–Herriot models for estimating small area indicators are introduced. Among the available procedures for fitting linear mixed models, the residual maximum likelihood (REML) is employed. The empirical best predictor (EBLUP) of the vector of area means is derived. An approximation to the matrix of mean squared crossed prediction errors (MSE) is given and four MSE estim...
متن کاملFunctional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price
Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...
متن کامل