Selecting the amount of smoothing in nonparametric regression estimation for complex surveys

Opsomer, Jean D.; Miller, Curtis

doi:10.1080/10485250500054642

Cited by 30 publications

(22 citation statements)

References 9 publications

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“…[8] Simulation done L = 500 times for each setting. Each time, we generate a population based on one of the four supermodels, then draw a sample using either SRS or Poisson sampling.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Buskirk and Lohr [6] presented finite-sample and asymptotic properties under several approaches for inference of a modified density estimator introduced by Buskirk [7] and Bellhouse and Stafford. [3] Opsomer and Miller [8] studied the selection of the amount of smoothing for the nonparametric regression component of a model-assisted estimator using a cross-validation criterion. Breidt et al [9] and Goga [10] proposed estimators of the population totals using smoothing splines.…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric regression estimators in complex surveys

Zhang

Christensen

Zheng

2013

Journal of Statistical Computation and Simulation

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In this article, we extend smoothing splines to model the regression mean structure when data are sampled through a complex survey. Smoothing splines are evaluated both with and without sample weights, and are compared with local linear estimator. Simulation studies find that nonparametric estimators perform better when sample weights are incorporated, rather than being treated as if iid. They also find that smoothing splines perform better than local linear estimator through completely data-driven bandwidth selection methods.

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“…[8] Simulation done L = 500 times for each setting. Each time, we generate a population based on one of the four supermodels, then draw a sample using either SRS or Poisson sampling.…”

Section: Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonparametric regression estimators in complex surveys

Zhang

Christensen

Zheng

2013

Journal of Statistical Computation and Simulation

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“…The bandwidth parameter for the model-based estimator is selected by minimizing the cross-validation function, The nonparametric regression estimators are based on the local polynomial regression estimator with p = 1. To ensure the estimator (2) is well-defined for every sample, we consider the ridge adjustment of Opsomer and Miller (2005), which is similar to Seifert and Gasser (2000) and consisting in the addition of the term ν N p+1 I (we use ν = 1). The resulting estimator is given by m j = e 1 (X s j W s j X s j + ν N p+1 I ) −1 X s j W s j Y s = w s j Y s .…”

Section: Empirical Studymentioning

confidence: 99%

“…For selecting the bandwidth parameter h with the model-assisted approach, we consider the design-based cross-validation criterion DC V , which was introduced by Opsomer and Miller (2005) and is given by…”

Section: Empirical Studymentioning

confidence: 99%

Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence

2010

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We address the problem of the estimation of the population mean and the distribution function using nonparametric regression. These methods are being used in a wide range of settings and areas of research. In particular, they are a good alternative to other classical methods in the survey sampling context, since they work under the assumption that the underlying regression function is smooth. Some relevant nonparametric regression methods in survey sampling are presented. Data on breast cancer prevalence derived from 40 European countries are used to study the application of the nonparametric estimators to the estimation of cancer prevalence. Result derived from an empirical study show that nonparametric estimators have a good empirical performance in this study on cancer prevalence.

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“…samples from the underlying population. There are only a few results available when the sampling design is different (e.g., [5,7,13,18,2,3,4] and [20]). The properties of nonparametric kernel density estimation based on i.i.d.…”

Section: Introductionmentioning

confidence: 97%

Nonparametric density estimation using partially rank-ordered set samples with application in estimating the distribution of wheat yield

Nazari¹,

Jozani²,

Kharrati-Kopaei³

2014

Electron. J. Statist.

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We study nonparametric estimation of an unknown density function f based on the ranked-based observations obtained from a partially rank-ordered set (PROS) sampling design. PROS sampling design has many applications in environmental, ecological and medical studies where the exact measurement of the variable of interest is costly but a small number of sampling units can be ordered with respect to the variable of interest by any means other than actual measurements and this can be done at low cost. PROS observations involve independent order statistics which are not identically distributed and most of the commonly used nonparametric techniques are not directly applicable to them. We first develop a kernel density estimator of f based on an imperfect PROS sampling procedure and study its theoretical properties. Then, we consider the problem when the underlying distribution is assumed to be symmetric and introduce some plug-in kernel density estimators of f . We use an EM type algorithm to estimate misplacement probabilities associated with an imperfect PROS design. Finally, we expand on various numerical illustrations of our results via several simulation studies and a case study to estimate the distribution of wheat yield using the total acreage of land which is planted in wheat as an easily obtained auxiliary information. Our results show that the PROS density estimate performs better than its SRS and RSS counterparts.

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Selecting the amount of smoothing in nonparametric regression estimation for complex surveys

Cited by 30 publications

References 9 publications

Nonparametric regression estimators in complex surveys

Nonparametric regression estimators in complex surveys

Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence

Nonparametric density estimation using partially rank-ordered set samples with application in estimating the distribution of wheat yield

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