A unified jackknife theory for empirical best prediction with M-estimation

By Jiming Jiang, Partha Lahiri, Shu-Mei Wan. 2002

197 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/295d7ef77f700317566be87cbbff86bfce3f36c6

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In Statistics and Probability

Citations in the corpus

Small Area Shrinkage Estimation (2012, methodology): “This happens for the ANOVA estimators suggested by Prasad and Rao (1990) and the REML estimators considered by Datta and Lahiri (2000).”
Mixed model prediction and small area estimation (2006, methodology): “The jackknife (see Jiang et al., 2002) and the parametric bootstrap methods (see Glickman and Pfeffermann, 2004; Lahiri, 2003a) are well posed to handle GLMMs for SAE.”
From start to finish: a framework for the production of small area official statistics (2018, methodology): “Jackknife methods have been developed for the same purpose under a wider range of models (Jiang et al., 2002).”
Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates (2009, methodology): “In this section, we obtain a nearly unbiased estimator of MSPE(̂EBi )=E(̂EBi − i)2, using the jackknife methods proposed by Jiang et al. (2002) and Chen & Lahiri (2002).”
Estimation of Finite Population Domain Means (2006, methodology): “…and Smith 1969; Datta and Ghosh 1991, among others), empirical Bayes method (e.g., Efron and Morris 1973; Morris 1983; Ghosh and Meeden 1986; Ghosh and Lahiri 1987; Arora, Lahiri, and Mukherjee 1997), and empirical best prediction method (e.g., Prasad and Rao 1999; Jiang, Lahiri, and Wan 2002).”
Bootstrap mean squared error of a small-area EBLUP (2008, methodology): “The jackknife methodology proposed by Jiang et al , 16 provides estimators with bias of order <inline-graphic xmlns:xlink="http://www.”
Small area estimates of labour force participation under a multinomial logit mixed model (2007, methodology): “Jiang et al. (2002) proposed a jackknife methodology for estimation under generalized linear mixed models.”
On parametric bootstrap methods for small area prediction (2006, methodology): “…bias-corrected estimator of MSEi: M̂SE bc i ≡M̂SEi − bi.β̂, ξ̂, η̂i/ n : .2:14/ This is the approach that was taken by, for example, Prasad and Rao (1990), Jiang et al. (2002) and Rao (2003), under the assumption that ψ is the identity and the distributions Q and R are both normal, which…”
New important developments in small area estimation (2013, methodology): “Jiang, Nguyen and Rao (2010) apply the method for selecting nonparametric P-spline models of the form (6.31).”
Nonparametric estimation of mean-squared prediction error in nested-error regression models (2005, unknown): “Jiang, Lahiri and Wan [19] proposed a jackknife-based bias correction of the mean-squared error estimator.”