On kernel-based quantile estimation using different stratified sampling schemes with optimal allocation

In nonparametric regression research, estimation of regression function is a prime concern. Recently, researchers developed some modified Nadaraya-Watson (N-W) regression estimators utilizing robust mean, median and harmonic mean. In this paper, we propose to utilize the additive combination of non-traditional measures i.e. (Hodges-Lehmann, Mid-Range, Tri-Mean, Quartile-Deviation) with the robust minimum covariance determinant (MCD) scale estimator in (N-W) regression estimator. Utilizing these measures, we get some new versions of (N-W) regression estimator. We also attempted to derive the properties of the proposed versions, such as bias, variance, mean square error (MSE), and mean integrated square error (MISE). The proposed estimators are compared with some of the existing estimators available in literature through a simulation study, utilizing two artificial populations. We also incorporated real-life application by taking British food data set denoted as Engel95, and assess the predictive ability of nonparametric regression, based on proposed and existing N-W estimators.

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“…There are many kernel functions available in literature however Gaussian is the most famous kernel function, see [2,5,7,8,22,23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nadaraya-Watson kernel regression estimators utilizing some non-traditional and robust measures: a numerical application of British food data

Shahzad¹,

Ahmad

Almanjahie

et al. 2023

Hacettepe Journal of Mathematics and Statistics

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“…Also, [29] used SRSS to improve distribution and quality estimations' performance. Moreover, [12] proposed a kernel-based estimator of the quantile based on SRSS with optimal allocation. Unlike the SRS case, a few numbers of papers addressed the OR problem using RSS scheme.…”

Section: Introductionmentioning

confidence: 99%

On Kernel-Based Estimator of Odds Ratio Using Different Stratified Sampling Schemes

Eftekharian

Samawi

Rochani

2022

Stat., optim. inf. comput.

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The kernel-based estimator of Cochran Mantel-Haenszel odds ratio based on stratified simple and ranked set sampling is proposed. The expectation and variance of the estimator are analytically obtained. Using a simulation study, the estimator based on stratified ranked set sampling is more efficient than its counterpart based on stratified simple random sampling. Finally, the estimator's performance is investigated by using base deficit data.

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On kernel-based estimation of distribution function and its quantiles based on ranked set sampling

Eftekharian

Samawi

Amiri

2022

Journal of Statistical Computation and Simulation

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On kernel-based quantile estimation using different stratified sampling schemes with optimal allocation

Cited by 4 publications

References 21 publications

Adaptive Nadaraya-Watson kernel regression estimators utilizing some non-traditional and robust measures: a numerical application of British food data

Adaptive Nadaraya-Watson kernel regression estimators utilizing some non-traditional and robust measures: a numerical application of British food data

On Kernel-Based Estimator of Odds Ratio Using Different Stratified Sampling Schemes

On kernel-based estimation of distribution function and its quantiles based on ranked set sampling

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