Bias Robust Estimation in Finite Populations Using Nonparametric Calibration

Chambers, Ray; Dorfman, Alan H.; Wehrly, Thomas E.

doi:10.1080/01621459.1993.10594319

Cited by 45 publications

(33 citation statements)

References 14 publications

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“…Dorfman (1992), Dorfman and Hall (1993) and Chambers, Dorfman and Wehrly (1993) study and extend these techniques to allow models to be correctly specified for a larger class of functions. Breidt and Opsomer (2000) first consider nonparametric models for ξ within a model-assisted framework and obtain a local polynomial regression estimator as a generalization of the ordinary generalized regression estimator.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Model Calibration Estimation in Survey Sampling

Montanari

Ranalli

2005

Journal of the American Statistical Association

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Calibration is commonly used in survey sampling to include auxiliary information at the estimation stage of a population parameter. Calibrating the observation weights on the population means (or totals) of a set of auxiliary variables means building weights that when applied to the auxiliaries give exactly their population mean (total). Implicitly calibration techniques rely on a linear relation between the survey variable and the auxiliary variables. However, when auxiliary information is available for all units in the population, more complex modelling can be handled by means of model calibration: the auxiliary variables are used to obtain fitted values of the survey variable for all units of the population and estimation weights are sought to satisfy calibration constraints on the fitted values population mean, rather than on the auxiliary variable ones. In this work we extend model calibration by assuming more general superpopulation models and employ nonparametric methods to obtain the fitted values to calibrate on. More precisely, we adopt neural network learning and local polynomial smoothing to estimate the functional relationship between the survey variable and the auxiliary variables. Neural

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Section: Introductionmentioning

confidence: 99%

Nonparametric Model Calibration Estimation in Survey Sampling

Montanari

Ranalli

2005

Journal of the American Statistical Association

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“…It is asymptotically model-unbiased for the finite population proportions P if the predicted values of the nonsample units are obtained by using the correct regression model. The expected value of the predictive estimator is then asymptotically equal to that of P (Royall 1970;Chambers et al 1993). Thus the two approaches represent two different paradigms of thinking.…”

Section: Introductionmentioning

confidence: 96%

“…Commonly used estimators for continuous responses are either kernel based (Kuo 1988;Kuk 1993;Dorfman and Hall 1993;Chambers et al 1993;Breidt and Opsomer 2000;Montanari and Ranalli 2005) or spline based (Little and Zheng 2004;Zheng and Little 2005;Breidt et al 2005;Opsomer et al 2007). For a recent review of such estimators in a complex survey, we refer to Breidt and Opsomer (2009).…”

Section: Introductionmentioning

confidence: 98%

Inference on finite population categorical response: nonparametric regression-based predictive approach

Adhya

Banerjee²,

Chattopadhyay

2011

AStA Adv Stat Anal

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Suppose that a finite population consists of N distinct units. Associated with the ith unit is a polychotomous response vector, d i , and a vector of auxiliary variable x i . The values x i 's are known for the entire population but d i 's are known only for the units selected in the sample. The problem is to estimate the finite population proportion vector P . One of the fundamental questions in finite population sampling is how to make use of the complete auxiliary information effectively at the estimation stage. In this article a predictive estimator is proposed which incorporates the auxiliary information at the estimation stage by invoking a superpopulation model. However, the use of such estimators is often criticized since the working superpopulation model may not be correct. To protect the predictive estimator from the possible model failure, a nonparametric regression model is considered in the superpopulation. The asymptotic properties of the proposed estimator are derived and also a bootstrapbased hybrid re-sampling method for estimating the variance of the proposed estimator is developed. Results of a simulation study are reported on the performances of the predictive estimator and its re-sampling-based variance estimator from the modelbased viewpoint. Finally, a data survey related to the opinions of 686 individuals on the cause of addiction is used for an empirical study to investigate the performance of the nonparametric predictive estimator from the design-based viewpoint.

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“…Different threads of research cover locating and trimming extreme sample weights (Potter 1988(Potter , 1990, controlling the effect of outliers on the estimation of descriptive population statistics, and constructing outlier-robust estimation techniques (Chambers et al 1993;Chambers 1996;Zaslavsky et al 2001). Henry and Valliant (2012) review much of this literature.…”

Section: Introductionmentioning

confidence: 99%

Linear Regression Diagnostics in Cluster Samples

Valliant

2015

Journal of Official Statistics

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An extensive set of diagnostics for linear regression models has been developed to handle nonsurvey data. The models and the sampling plans used for finite populations often entail stratification, clustering, and survey weights, which renders many of the standard diagnostics inappropriate. In this article we adapt some influence diagnostics that have been formulated for ordinary or weighted least squares for use with stratified, clustered survey data. The statistics considered here include DFBETAS, DFFITS, and Cook's D. The differences in the performance of ordinary least squares and survey-weighted diagnostics are compared using complex survey data where the values of weights, response variables, and covariates vary substantially.

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Bias Robust Estimation in Finite Populations Using Nonparametric Calibration

Cited by 45 publications

References 14 publications

Nonparametric Model Calibration Estimation in Survey Sampling

Nonparametric Model Calibration Estimation in Survey Sampling

Inference on finite population categorical response: nonparametric regression-based predictive approach

Linear Regression Diagnostics in Cluster Samples

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