Small area estimation

By Danny Pfeffermann. 2011

1,056 citations recorded by the source corpus.

Subfields: Finance, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/64790cd0bf3d9534b5bdbe1ea877e2b40ae2e80f

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Citations in the corpus

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009, methodology): “…try to account for the variability in P̂, but formal inference is usually approximate (e.g., Morris 1983a, b); one commonly used approximation is based on multivariable differential calculus and Taylor-series expansions of estimators (e.g., Prasad and Rao 1990, Cressie 1992, Rao 2003:section 6.2).”
A Bayesian Analysis of Body Mass Index Data From Small Domains Under Nonignorable Nonresponse and Selection (2010, methodology): “We exclusively analyze the NHANES III data and a Bayesian method that uses smallarea estimation techniques (Rao 2003) with a spline regression to predict the percentiles at each age (2–19 years).”
Calibrated Bayes, an alternative inferential paradigm for official statistics (2012, methodology): “DMC applies design-based inference for descriptive statistics like means and totals in large samples, and models are used for small area estimation, to handle survey nonresponse, and in some specialized areas like time series analysis (e.g. Kalton 2002; Rao 2003, 2011).”
Small area estimation of labour force indicators under a multinomial model with correlated time and area effects (2015, background): “Some descriptions of small area estimation theory can be found in Rao (2003), or in Ghosh and Rao (1994), Rao (1999), Pfeffermann (2002, 2013) and Jiang and Lahiri (2006).”
Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error (2015, background): “Consequently, model-based estimation of small area means has been receiving a lot of attention in statistical literature where the goal is to extract information from other sources; see Rao (2003), Datta (2009) and Pfefferman (2013) for a complete review.”
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach (2014, methodology): “Hierarchical Bayesian models have been extensively used in small area estimation; see Rao (2003), Chapter 10 and Datta (2009).”
Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation (2017, background): “Similar problems are regularly encountered in small area estimation, where a model-based approach is pursued (Rao, 2005).”
Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates (2009, methodology): “…̂EBi , the GSK predictor ̃ EB i and the naive EB predictor ̂EBi,na, obtained by ignoring the measurement errors and applying the standard method of Battese et al. (1988) and Prasad & Rao (1990) based on the fitting-of-constants method to estimate the variance components (Rao, 2003, section 7.2.1).”
Small area estimation of poverty indicators (2010, positive): “Therefore, small area estimation techniques are required that “borrow strength” across areas through linking models based on auxiliary information coming from censuses or administrative registers; see Rao (2003) for a comprehensive account of these techniques.”
Robust small area estimation (2009, methodology): “When the auxiliary variables are discrete, alternatively, one can use Mallows weights described in de Jongh, de Wet & Welsh (1988). These weights are nonparametric in the sense that no assumption is needed for the distribution of the auxiliary variables.”
Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, background): “For a small area of interest (AOI, a finite set of units for which an estimate is desired), a direct estimate (prediction) of the total of an attribute from a sample within the AOI is often discouraged by a small sample size (Hansen et al., 1983; Rao, 2003).”
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.”
Best Predictive Small Area Estimation (2011, methodology): “Still, the model-based BP is routinely used for SAE under the nested-error regression model due to the anticipated connection between the response and the available covariates (e.g., Rao 2003).”
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.”
Targeting energy justice: Exploring spatial, racial/ethnic and socioeconomic disparities in urban residential heating energy efficiency (2016, methodology): “Since many of the variables identified in the RECS can also be found in the Census ACS, relationships derived from the statistical model, known as direct estimators, can be applied to the block group level dataset as indirect estimators for constructing small-area estimates, under the assumption tha”
New important developments in small area estimation (2013, methodology): “ei ∼ N(0, σ 2 Di), which implies E(δi) = 1 and ui |σ 2 u ∼ t(k)(0, σ 2 u (k − 2)/k). The coefficient δi is distributed around 1, inflating or deflating the variance of ui = θi − x′iβ . A large value δi signals the existence of an outlying area mean θi . The degrees of freedom parameter, k, is taken ”
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).”