Results 161 to 170 of about 1,708 (192)

Insensitivity of Nadaraya–Watson estimators to design correlation

Communications in Statistics - Theory and Methods, 2021
We show that Nadaraya–Watson estimators in nonparametric regression may be uniformly consistent without any specification of the design correlation structure.
Yuliana Linke, Igor Borisov
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Towards Insensitivity of Nadaraya--Watson Estimators to Design Correlation

Theory of Probability & Its Applications, 2023
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Bootstrapping stationary sequences by the Nadaraya-Watson regression estimator

Journal of Nonparametric Statistics, 2002
We propose a new bootstrap method for stationary time series data which uses the Nadaraya-Watson kernel regression estimator. We call this method N-W bootstrap. The N-W bootstrap consists of estimating the conditional autoregressive mean function by the Nadaraya-Watson estimator and bootstrapping the resulting residuals. Indeed we obtain a bootstrapped
Cheolyong Park, Tae Yoon Kim
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On the Nadaraya–Watson kernel regression estimator for irregularly spaced spatial data

Journal of Statistical Planning and Inference, 2020
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Xiequan Fan
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On asymptotic behavior of Nadaraya–Watson regression estimator

Communications in Statistics - Theory and Methods, 2016
ABSTRACTThe random field (Xi, Yi), is dependent and assumed to satisfy some mild mixing conditions. In this article, a Nadaraya–Watson regression estimator (N. W. R. E.) is established and investigated. We show that the distribution of the N. W. R. E. is asymptotically normal under some commonly used conditions.
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Weighted Nadaraya–Watson regression estimation

Statistics & Probability Letters, 2001
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Asymptotic normality of the Nadaraya–Watson semivariogram estimators

TEST, 2007
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A generalized Nadaraya–Watson estimator for observations obtained from a mixture

Theory of Probability and Mathematical Statistics, 2020
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Dychko, H. M., Maĭboroda, R. E.
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A NEW MODIFICATION TO THE ADAPTIVE NADARAYA-WATSON KERNEL REGRESSION ESTIMATOR

Advances and Applications in Statistics, 2016
Summary: In this paper, a modification to the adaptive Nadaraya-Watson (NW) kernel regression estimator based on a function of the range of pilot density estimates and the sample size with varying bandwidths is proposed. The performance of proposed estimator in terms of mean squared error (MSE) is compared with NW and modified NW estimators.
Joshi, Venkatesh B., Deshpande, Bhargavi
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The uniform convergence of the nadaraya‐watson regression function estimate

Canadian Journal of Statistics, 1978
AbstractIf (X1,Y1), …, (Xn,Yn) is a sequence of independent identically distributed Rd × R‐valued random vectors then Nadaraya (1964) and Watson (1964) proposed to estimate the regression function m(x) = ϵ {Y1|X1 = x{ by where K is a known density and {hn} is a sequence of positive numbers satisfying certain properties.
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