Mixed model prediction and small area estimation

By Jiming Jiang, Partha Lahiri. 2006

388 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e082b46408e8bc1dab4b0c1163f78bf807e9371e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Best Predictive Small Area Estimation (2011, background): “The empirical best linear unbiased prediction, or EBLUP, is well known in small area estimation (SAE, e.g., Rao 2003; Jiang and Lahiri 2006).”
Smooth Transition from Fixed Effects to Mixed Effects Models in Multi-level regression Models (2008, background): “More recently they have also attracted a good amount of attention in small area statistics, see Ghosh and Rao (1994), Rao (2003), Jiang and Lahiri (2006) for reviews of small-area techniques.”
Hierarchical Bayes Modeling of Survey-Weighted Small Area Proportions (2014, neutral): “See Rao (2003) and Jiang and Lahiri (2006a) for reviews, and Chattopadhyay et al. (1999), Farrell et al. (1997), and Malec et al. (1997, 1999) for methods specifically for estimating small area proportions.”
Semiparametric inference in generalized mixed effects models (2008, methodology): “(1982), Breslow and Clayton (1993), Lee and Neider (1996), Malee et al (1997), Ghosh et al.”
Small area estimates of labour force participation under a multinomial logit mixed model (2007, background): “For a recent review, see Jiang and Lahiri (2006).”
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach (2014, methodology): “The monograph of Rao (2003) contains a comprehensive account of small area estimation techniques that appeared until the publication date; see Jiang and Lahiri (2006), Datta (2009) and Pfeffermann (2013) for reviews of the more recent work.”
Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models (2008, methodology): “For a good review on small area and linear mixed model research, the readers are referred to the book by Rao (2003), and two recent review papers by Rao (2005) and Jiang and Lahiri (2006).”
Robust small area estimation (2009, background): “Recent review articles on small area estimation include Rao (2005) and Jiang & Lahiri (2006).”
Multinomial-based small area estimation of labour force indicators* (2013, neutral): “See a description of this theory in the monograph of Rao (2003), or in the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002) and Jiang and Lahiri (2006).”
New important developments in small area estimation (2013, methodology): “For this model, there is no explicit expression for the best predictor (BP) under a quadratic loss function, that is, for p̂BPi = E(pi|yi,xi;ψ), but as shown in Jiang and Lahiri (2006b), the BP can be computed (approximated) numerically as the ratio of two onedimensional integrals.”
Bootstrap mean squared error of a small-area EBLUP (2008, methodology): “Using similar ideas as in Butar and Lahiri [25] and Pfeffermann and Tiller [17], but applied to the bootstrap procedure for finite populations described above, a second bootstrap estimator of MSE(η̂E) is proposed.”
Small area estimation of labour force indicators under a multinomial model with correlated time and area effects (2015, methodology): “This is the model that was studied by López-Vizcaı́no et al. (2013). Model 0 is obtained by making u1,d1 =u1,d2 in model 1. This is the model that was studied by Molina et al. (2007). To fit the models we combine the penalized quasi-likelihood method, which was introduced by Breslow and Clayton (199”
Empirical Best Prediction Under Unit-Level Logit Mixed Models (2016, background): “See the monographs of Rao (2003) and Rao and Molina (2015), and the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002, 2013), and Jiang and Lahiri (2006) for an introduction to SAE.”
Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, neutral): “2003, 2006), Pfeffermann (2002), Jiang and Lahiri (2006), and Datta (2009).”
Empirical best prediction under area-level Poisson mixed models (2016, methodology): “See the monograph Rao (2003) and the reviews of Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002), Jiang and Lahiri (2006), Rao (2008) and Pfeffermann (2013) for an introduction to the SAE.”