منابع مشابه
Parametric Bootstrap Procedures for Small Area Prediction Variance
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.
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Many variables of interest in business and agricultural surveys have skewed distributions. An example from the National Agricultural Statistics Service is the acres harvested for a particular crop. We investigate small area estimation methods for skewed data under the assumption that a lognormal model is a reasonable approximation for the distribution of the response given covariates. Empirical...
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Best linear unbiased prediction is well known for its wide range of applications including small area estimation. While the theory is well established for mixed linear models and under normality of the error and mixing distributions, the literature is sparse for nonlinear mixed models under nonnormality of the error or of the mixing distributions. This article develops a resampling based unifie...
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Small area estimation has received enormous attention in recent years due to its wide range of application, particularly in policy making decisions. The variance based on direct sample size of small area estimator is unduly large and there is a need of constructing model based estimator with low mean squared prediction error (MSPE). Estimation of MSPE and in particular the bias correction of MS...
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Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific estimators may not provide adequate precision because sample sizes in small areas are seldom large enough. This makes it necessary to employ indirect estimators based on linking models. Basic area level and unit level models have been extensiv...
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ژورنال
عنوان ژورنال: Canadian Journal of Statistics
سال: 2018
ISSN: 0319-5724,1708-945X
DOI: 10.1002/cjs.11461