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456
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 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
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
Abstract 2005 Special Issue An
"... In this paper, we consider learning problems defined on graphstructured data. We propose an incremental supervised learning algorithm for networkbased estimators using diffusion kernels. Diffusion kernel nodes are iteratively added in the training process. For each new node added, the kernel funct ..."
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function center and the output connection weight are decided according to an empirical risk driven rule based on an extended chained version of the Nadaraja–Watson estimator. Then the diffusion parameters are determined by a geneticlike optimization technique.
Function Approximation from Noisy Data by an Incremental Rbf Network
 Pattern Recognition
, 1999
"... this paper we propose an incremental RBF network for function approximation from noisy data. Hidden gaussian nodes are iteratively added in the training process. For each new added node, the activation function center and the output connection weight are settled according to an extended chained vers ..."
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Cited by 4 (0 self)
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version of the NadarajaWatson estimator [1]. Then the variances of the activation functions are determined by an empirical risk driven rule based on a geneticlike optimization technique [2]. Assuming the data affected by noise, an optimal network approximation must provide a residual error at least
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
Monetary Policy in a Data Rich Environment
 Journal of Monetary Economics
, 2002
"... Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasi ..."
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Cited by 149 (3 self)
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the feasibility of incorporating richer information sets into the analysis, both positive and normative, of Fed policymaking. We employ a factormodel approach, developed by Stock and Watson (1999a,b), that permits the systematic information in large data sets to be summarized by relatively few estimated factors
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 ..."
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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
The Generalized Dynamic Factor Model: onesided estimation and forecasting
"... This paper proposes a new forecasting method which makes use of information from a large panel of time series. As in Forni, Hallin, Lippi and Reichlin (2000), and in Stock and Watson (2002a,b), the method is based on a dynamic factor model. We argue that our method improves upon a standard principal ..."
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Cited by 102 (7 self)
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This paper proposes a new forecasting method which makes use of information from a large panel of time series. As in Forni, Hallin, Lippi and Reichlin (2000), and in Stock and Watson (2002a,b), the method is based on a dynamic factor model. We argue that our method improves upon a standard
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
The Width Of GaltonWatson Trees
, 1999
"... . It is proved that the moments of the width of GaltonWatson trees with offspring variance oe are asymptotically given by (oe p n) p mp where mp are the moments of the maximum of the local time of a standard scaled Brownian excursion. This is done by combining a weak limit theorem and a tightne ..."
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Cited by 3 (3 self)
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tightness estimate. The method is quite general and we state some further applications. 1. Introduction In this paper we are considering rooted trees which are family trees of a GaltonWatson branching process conditioned to have total progeny n. Without loss of generality we may assume that the offspring
Results 1  10
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456