On parametric bootstrap methods for small area prediction

By Peter Hall, Tapabrata Maiti. 2006

249 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b2b50eb6335a3637de116496a1c474fe77940115

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Citations in the corpus

Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, background): “See, for example, the work of Battese, Harter, and Fuller (1988), Prasad and Rao (1990), Datta and Ghosh (1991), Singh, Stukel, and Pfeffermann (1998), Datta and Lahiri (2000), Hall and Maiti (2006a, 2006b), and Chatterjee, Lahiri, and Li (2008).”
Small-Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas (2007, methodology): “[See Hall and Maiti 2006 for bootstrap bias corrections in small-area estimation that warrant MSE estimation of order O(1/m2).]”
Prediction in multilevel generalized linear models (2009, methodology): “An alternative approach is to use bootstrapping to correct the bias of analytical expansions for the prediction error variance (see the references in Hall and Maiti (2006)).”
Small area estimates of labour force participation under a multinomial logit mixed model (2007, methodology): “Hall and Maiti (2006) proposed a double-bootstrap approach for bias correction, which is applicable for constructing bias-corrected estimators of the meansquared error and for computing prediction regions under general settings. Under logistic mixed models, González-Manteiga et al. (2007) proposed a”
Bootstrap mean squared error of a small-area EBLUP (2008, methodology): “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.”
Mixed model prediction and small area estimation (2006, methodology): “The nonparametric bootstrap method proposed by Hall and Maiti (2006a) clearly offers an alternative to the robust delta method mentioned above and the jackknife method of Jiang et al. (2002).”
Non‐parametric small area estimation using penalized spline regression (2008, methodology): “Recent work on parametric bootstrap methods includes Hall and Maiti (2006), who proposed a double-bootstrap procedure to construct a second-order correct estimator of the PMSE.”
Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models (2008, methodology): “Hall and Maiti (2006b) obtain that if ˆ I α is calibrated once (twice), the coverage accuracy improves to O ( m − 2 ) [ O ( m − 3 )].”
Small area estimation of poverty indicators (2010, unknown): “The double bootstrap method of Hall and Maiti (2006) could provide a better MSE estimator in terms of relative bias, but for large populations this method might not be computationally feasible.”
Robust small area estimation (2009, methodology): “Methods studied include a linearization method of Prasad & Rao (1990), a jackknife method of Jiang, Lahiri & Wan (2002), and a parametric bootstrap method of Hall & Maiti (2006).”
Multinomial-based small area estimation of labour force indicators* (2013, methodology): “In this paper we follow the resampling approach appearing in González-Manteiga et al. (2007, 2008a, 2008b) and Hall and Maiti (2006) to introduce a parametric bootstrap procedure.”
Small Area Shrinkage Estimation (2012, background): “Further references to this lite- rature may be found in Pfeffermann and Tiller (2005), Lahiri (2003) and Hall and Maiti (2006).”