A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data

By Changbao Wu, Randy R. Sitter. 2001

387 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/81c20afc4a9997ac2e2e167d32d9db196214c52d

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In Statistics and Probability

Citations in the corpus

The Generalized Oaxaca-Blinder Estimator (2020, methodology): “The idea of using fitted values in an OLS regression has also appeared before in the survey sampling community, under the name “model calibration estimator” (Wu and Sitter, 2001).”
ESTIMATION OF DISTRIBUTION FUNCTION AND QUANTILES USING THE MODEL-CALIBRATED PSEUDO EMPIRICAL LIKELIHOOD METHOD (2002, methodology): “The model-calibrated pseudo empirical maximum likelihood (MPEML) estimator ̂ Y EL (Wu and Sitter, 2001) is obtained by first adapting a superpopulation model and then calibrating over fitted values from the model.”
Model-Assisted Survey Regression Estimation with the Lasso (2017, methodology): “Among many examples, see Zou (2006), Bunea, Tsybakov, and Wegkamp (2007), Van De Geer and Bühlmann (2009), Raskutti, Wainwright, and Yu (2011), and the references therein.”
Calibration approach estimators in stratified sampling (2007, methodology): “Wu and Sitter (2001) and Sitter and Wu (2002) generalize the calibration procedure by means of model calibrations and extend a pseudo-empirical likelihood method to obtain efficient estimator of quadratic and other second-order finite population functions.”
Multiply robust imputation procedures for the treatment of item nonresponse in surveys (2017, methodology): “The calibration constraints (5)–(7) are similar to those encountered in the context of model calibration for complete data (Wu & Sitter, 2001).”
Model-Assisted Estimation of Forest Resources With Generalized Additive Models (2007, methodology): “Recent advances in the use of auxiliary information include nonlinear estimation (Wu and Sitter 2001), nonparametric survey regression estimation (Kuo 1988; Dorfman 1992; Dorfman and Hall 1993; Chambers, Dorfman, and Wehrly 1993; Breidt and Opsomer 2000), and the calibration point of view (Deville a”
Selecting the amount of smoothing in nonparametric regression estimation for complex surveys (2005, background): “Recently, Wu and Sitter [4] introduced a nonlinear version of model-assisted estimation, which can be useful if the linear model is not appropriate.”
Doubly Robust Inference With Nonprobability Survey Samples (2018, methodology): “Conditions C2 and C3 are the usual smoothness and boundedness conditions (Wu and Sitter, 2001).”
The Augmented Synthetic Control Method (2018, background): “framework of Wu and Sitter (2001). Let m̂(Xi) = X ′ i·η̂, where η̂ are the coefficients of a ridge regression of control post-treatment outcomes Y0 on centered pre-treatment outcomes X0· with penalty hyper-parameter λ.”
Calibration Estimation in Survey Sampling (2010, neutral): “Wu and Sitter (2001) considered the following superpopulation model and proposed the model calibration estimator using the calibration equation where ˆ µ i = µ ( x i , ˆ θ ) and considered a variance estimator for a simple situation.”