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INTRODUCTION TO THE VARIANCE-STABILIZING BANDWIDTH FOR THE NADARAYA-WATSON REGRESSION ESTIMATOR
INTRODUCTION TO THE VARIANCE-STABILIZING BANDWIDTH FOR THE NADARAYA-WATSON REGRESSION ESTIMATORIn linear regression under heteroscedastic variances, Aitken estimator is employed to account for the differences in variances. Employing the same principle, we propose the Nadaraya-Watson regression estimator with variable variance-stabilizing bandwidth (VS bandwidth) that minimizes asymptotic MISE (AMISE) while maintaining asymptotic homoscedasticity.
金澤, 雄一郎 +5 more
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Threshold reweighted Nadaraya–Watson estimation of jump-diffusion models
Probability, Uncertainty and Quantitative Risk, 2022 <p style='text-indent:20px;'>In this paper, we propose a new method to estimate the diffusion function in the jump-diffusion model. First, a threshold reweighted Nadaraya–Watson-type estimator is introduced. Then, we establish asymptotic normality for the estimator and conduct Monte Carlo simulations through two examples to verify the better ...
Song, Kunyang +2 more
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Adaptive weighted Nadaraya–Watson estimation of the conditional quantiles by varying bandwidth [PDF]
Communications in Statistics - Simulation and Computation, 2020 In this paper, we define the adaptive Weighted Nadaraya–Watson estimation (AWNW) of the conditional distribution function (cdf) for independent and identically distributed (iid) data using varying ...
El Shekh Ahmed, Hazem I. +2 more
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Robust localization based on non‐parametric kernel technique
Electronics Letters, 2022 Parametric approaches are primarily used in the context of robust localization. However, the localization performance is degraded when there is a mismatch between the assumed model and the actual situation.
Chee‐Hyun Park, Joon‐Hyuk Chang
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K-Nearest Neighbor Method with Principal Component Analysis for Functional Nonparametric Regression
مجلة بغداد للعلوم, 2022 This paper proposed a new method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate
Shelan Saied Ismaeel +2 more
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Empirical Density Estimation for Interval Censored Data
Austrian Journal of Statistics, 2016 This paper is concerned with the nonparametric estimation of a density function when the data are incomplete due to interval censoring. The Nadaraya-Watson kernel density estimator is modified to allow description of such interval data.
Eugenia Stoimenova
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Nonparametric estimate remarks
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2006 Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model.
Jitka Poměnková
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A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
Mathematics, 2023 In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by LR fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as ...
Gholamreza Hesamian +2 more
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The Graphical Nadaraya-Watson Estimator on Latent Position Models
CoRR, 2023 Given a graph with a subset of labeled nodes, we are interested in the quality of the averaging estimator which for an unlabeled node predicts the average of the observations of its labeled neighbors. We rigorously study concentration properties, variance bounds and risk bounds in this context.
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An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
Stats, 2020 The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity. Its asymptotic bias has been studied by Rosenblatt in 1969 and has been reported in several related literature.
Samuele Tosatto, Riad Akrour, Jan Peters
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