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129,855
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 4 (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 16 (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 ..."
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Cited by 2375 (40 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 Nadarayaâ€“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.
Estimation and Inference in Econometrics
, 1993
"... The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas o ..."
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Cited by 1151 (3 self)
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The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, and bootstrap confidence intervals. Although bootstrapping often works well, it does not do so in every case.
A Simple Estimator of Cointegrating Vectors in Higher Order Cointegrated Systems
 ECONOMETRICA
, 1993
"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."
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Cited by 507 (3 self)
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Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions
Maximum Likelihood Linear Transformations for HMMBased Speech Recognition
 Computer Speech and Language
, 1998
"... This paper examines the application of linear transformations for speaker and environmental adaptation in an HMMbased speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias ..."
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Cited by 538 (65 self)
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) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as featurespace transforms). Reestimation formulae for all appropriate cases of transform are given. This includes a new and efficient "full" variance transform and the extension
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Results 1  10
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129,855