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470
NadarayaWatson Estimator for Sensor Fusion
, 1997
"... In a system of N sensors, the sensor S j , j = 1; 2 : : : ; N , outputs Y (j) 2 [0; 1], according to an unknown probability density p j (Y (j) jX), corresponding to input X 2 [0; 1]. A training nsample (X 1 ; Y 1 ), (X 2 ; Y 2 ), : : :, (X n ; Y n ) is given where Y i = (Y (1) i ; Y (2) i ..."
Abstract

Cited by 5 (2 self)
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a family of functions F with uniformly bounded modulus of smoothness, where Y = (Y (1) ; Y (2) ; : : : ; Y (N) ). Let f minimize I(:) over F ; f cannot be computed since the underlying densities are unknown. We estimate the sample size sufficient to ensure that NadarayaWatson estimator
Weighted NadarayaWatson estimation of conditional expected shortfall
 Journal of Financial Econometrics
, 2012
"... This paper addresses the problem of nonparametric estimation of the conditional expected shortfall (CES) which has gained popularity in nancial risk management. We propose a new nonparametric estimator of the CES. The proposed estimator is de ned as a conditional counterpart of the sample average es ..."
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Cited by 5 (0 self)
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estimator of the unconditional expected shortfall, where the empirical distribution function is replaced by the weighted NadarayaWatson estimator of the conditional distribution function. We establish asymptotic normality of the proposed estimator under an mixing condition. The asymptotic results reveal
On the asymptotic behavior of the NadarayaWatson estimator associated with the recursive SIR method
, 2012
"... Abstract. We investigate the asymptotic behavior of the NadarayaWatson estimator for the estimation of the regression function in a semiparametric regression model. On the one hand, we make use of the recursive version of the sliced inverse regression method for the estimation of the unknown param ..."
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Cited by 4 (0 self)
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Abstract. We investigate the asymptotic behavior of the NadarayaWatson estimator for the estimation of the regression function in a semiparametric regression model. On the one hand, we make use of the recursive version of the sliced inverse regression method for the estimation of the unknown
Weighted NadarayaWatson Regression Estimation
 Statistics and Probability Letters
, 2001
"... In this article we study nonparametric estimation of regression function by using the weighted NadarayaWatson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for ffmixing time series at both boundary and interior points, and we show that the estimato ..."
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Cited by 17 (1 self)
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In this article we study nonparametric estimation of regression function by using the weighted NadarayaWatson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for ffmixing time series at both boundary and interior points, and we show
Mean shift: A robust approach toward feature space analysis
 In PAMI
, 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
Abstract

Cited by 2395 (37 self)
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the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust Mestimators
unknown title
, 2007
"... Asymptotic normality of the NadarayaWatson estimator for nonstationary functional data and applications to telecommunications. ..."
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Asymptotic normality of the NadarayaWatson estimator for nonstationary functional data and applications to telecommunications.
Hierarchical Modelling and Analysis for Spatial Data. Chapman and Hall/CRC,
, 2004
"... Abstract Often, there are two streams in statistical research one developed by practitioners and other by main stream statisticians. Development of geostatistics is a very good example where pioneering work under realistic assumptions came from mining engineers whereas it is only now that statisti ..."
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Cited by 442 (45 self)
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selection, statistical inference and providing measures of uncertainty of the estimated parameters. Historically, the following observation of Watson (1986) is a key in understanding the development of statistical geostatistics: "In the mid 1970s the work of Georges Matheron and Jean Serra
Central limit theorems for the integrated squared error of derivative estimators
"... Abstract A central limit theorem for the weighted integrated squared error of kernel type estimators of the first two derivatives of a nonparametric regression function is proved by using results for martingale differences and Ustatistics. The results focus on the setting of the NadarayaWatson est ..."
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Abstract A central limit theorem for the weighted integrated squared error of kernel type estimators of the first two derivatives of a nonparametric regression function is proved by using results for martingale differences and Ustatistics. The results focus on the setting of the NadarayaWatson
Estimation of the Density and the Regression Function Under Mixing Conditions
, 1998
"... In this paper we derive rates of strong convergence for the kernel density estimator and for the NadarayaWatson estimator under the alphamixing condition and under the condition of absolute regularity. A combination of an inequality of Bernstein type (Rio 1995) and an exponential inequality (cf. F ..."
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Cited by 13 (3 self)
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In this paper we derive rates of strong convergence for the kernel density estimator and for the NadarayaWatson estimator under the alphamixing condition and under the condition of absolute regularity. A combination of an inequality of Bernstein type (Rio 1995) and an exponential inequality (cf
Estimation in Nonparametric Regression with Nonregular Errors
"... Abstract For sufficiently nonregular distributions with bounded support, the extreme observations converge to the boundary points at a faster rate than the square root of the sample size. In a nonparametric regression model with such a nonregular error distribution, this fact can be used to constru ..."
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to construct an estimator for the regression function that converges at a faster rate than the NadarayaWatson estimator. We explain this in the simplest case, review corresponding results from boundary estimation that are applicable here, and discuss possible improvements in parametric and semiparametric
Results 1  10
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470