Results 1 - 10 of 470

Nadaraya-Watson Estimator for Sensor Fusion

by Nageswara S. V. Rao, Nageswara S. V. Rao , 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 n-sample (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) - Add to MetaCart
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 Nadaraya-Watson estimator

Weighted Nadaraya-Watson estimation of conditional expected shortfall

by Kengo Kato - 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 ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
estimator of the uncondi-tional expected shortfall, where the empirical distribution function is replaced by the weighted Nadaraya-Watson estimator of the conditional distribution function. We establish asymptotic normality of the proposed estimator under an -mixing con-dition. The asymptotic results reveal

On the asymptotic behavior of the Nadaraya-Watson estimator associated with the recursive SIR method

by Bernard Bercu, Thi Mong, Ngoc Nguyen, Jerome Saracco , 2012
"... Abstract. We investigate the asymptotic behavior of the Nadaraya-Watson esti-mator 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 ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Abstract. We investigate the asymptotic behavior of the Nadaraya-Watson esti-mator 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 Nadaraya-Watson Regression Estimation

by Zongwu Cai - Statistics and Probability Letters , 2001
"... In this article we study nonparametric estimation of regression function by using the weighted Nadaraya-Watson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for ff-mixing time series at both boundary and interior points, and we show that the estimato ..."
Abstract - Cited by 17 (1 self) - Add to MetaCart
In this article we study nonparametric estimation of regression function by using the weighted Nadaraya-Watson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for ff-mixing time series at both boundary and interior points, and we show

Mean shift: A robust approach toward feature space analysis

by Dorin Comaniciu, Peter Meer - 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) - Add to MetaCart
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 M-estimators

unknown title

by unknown authors , 2007
"... Asymptotic normality of the Nadaraya-Watson estimator for non-stationary functional data and applications to telecommunications. ..."
Abstract - Add to MetaCart
Asymptotic normality of the Nadaraya-Watson estimator for non-stationary functional data and applications to telecommunications.

Hierarchical Modelling and Analysis for Spatial Data. Chapman and Hall/CRC,

by S Banerjee , B P Carlin , A E Gelfand , Chapman , / Hall , New Crc , N York; Cressie , P J Diggle , P J Ribeiro Jr , B D Ripley , 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 ..."
Abstract - Cited by 442 (45 self) - Add to MetaCart
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

by Melanie Birke
"... 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 U-statistics. The results focus on the setting of the NadarayaWatson est ..."
Abstract - Add to MetaCart
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 U-statistics. The results focus on the setting of the NadarayaWatson

Estimation of the Density and the Regression Function Under Mixing Conditions

by Eckhard Liebscher , 1998
"... In this paper we derive rates of strong convergence for the kernel density estimator and for the Nadaraya-Watson estimator under the alpha-mixing condition and under the condition of absolute regularity. A combination of an inequality of Bernstein type (Rio 1995) and an exponential inequality (cf. F ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
In this paper we derive rates of strong convergence for the kernel density estimator and for the Nadaraya-Watson estimator under the alpha-mixing 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

by Ursula U Müller , Wolfgang Wefelmeyer
"... 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 ..."
Abstract - Add to MetaCart
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 of 470