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The spectrum of kernel random matrices
spectrum kernel random matrices high-dimensional statisticalinference
2010/3/9
We place ourselves in the setting of high-dimensional statistical
inference where the number of variables p in a dataset of interest is
of the same order of magnitude as the number of observations n...
Note on asymptotic normality of kernel density estimator for linear process under short-range dependence
asymptotic normality kernel density estimator linear process short-range dependence
2009/9/21
Wc mnf;i&r the paablem of density estimation for
m a one-sided linear prosees X, = zt _ , a, Z, , with i.id square iategra-
Me kovatims - We prove that under weak contritions on
(ai)&, which imply ...
The use of variable kernel mass in density estimation
variable kernel mass density estimation
2009/9/21
The use of variable kernel mass in density estimation。
ADAPTIVE KERNEL ESTIMATION OF THE MODE IN A NONPARAMETRIC RANDOM DESIGN REGRESSION MODEL
Nonparametric regression random design mode kernel smoothing Nadaraya-Watson estimator
2009/9/21
In a nonparametric regession model with random design,
where the regression function m is given by rn (x). = E (Y I X = x),
estimation of the location 0 (mode) and size m(B) of a unique maximum
of ...
Kernel Estimators for Semi-Markov Processes。
Skewed Distributions Generated by the Cauchy Kernel
Skewed Distributions the Cauchy Kernel
2009/9/17
Skewed Distributions Generated by the Cauchy Kernel。
Kullback Leibler property of kernel mixture priors in Bayesian density estimation
Bayesian density estimation Dirichlet process, kernel mixture KullbackLeibler property posterior consistency
2009/9/16
Positivity of the prior probability of Kullback-Leibler neighborhood around the true density, commonly known as the Kullback-Leibler property, plays a fundamental role in posterior consistency. A popu...
Explicit connections between longitudinal data analysis and kernel machines
Best linear unbiased prediction classification generalized linear mixed models machine learning linear mixed models reproducing kernel Hilbert spaces
2009/9/16
Two areas of research – longitudinal data analysis and kernel machines – have large, but mostly distinct, literatures. This article shows explicitly that both fields have much in common with each othe...
A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data
Kernel estimator quantile function rate of convergence strong mixing strong uniform consistency truncated data
2009/9/16
In this paper we study some asymptotic properties of the kernel conditional quantile estimator with randomly left-truncated data which exhibit some kind of dependence. We extend the result obtained by...
Approximation for general bootstrap of empirical processes with an application to kernel-type density estimation
General bootstrap Brownian bridge Best approximation kernel density estimator
2010/3/19
The purpose of this note is to provide an approximation for the generalized bootstrapped empirical process achieving the rate in Komlós et al. (1975). The proof is based onmuch the same arguments used...
LINKING PARETO-TAIL KERNEL GOODNESS-OFFIT STATISTICSWITH TAIL INDEX AT OPTIMAL THRESHOLD AND SECOND ORDER ESTIMATION
extreme value statistics Pareto-type distribution goodness-of-fit threshold selection
2009/2/25
In this paper the relation between goodness-of-fit testing and the optimal selection of
the sample fraction for tail estimation, for instance using Hill’s estimator, is examined.
We consider this pr...
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Lanczos Approximations Speedup Kernel Partial Least Squares Regression
2010/3/18
The runtime for Kernel Partial Least Squares (KPLS) to compute the fit is quadratic
in the number of examples. However, the necessity of obtaining sensitivity measures as
degrees of freedom for mode...
Kernel methods in machine learning
Machine learning reproducing kernels support vector machines graphical models
2010/4/26
We review machine learning methods employing positive definite
kernels. These methods formulate learning and estimation problems
in a reproducing kernel Hilbert space (RKHS) of functions defined
on...
Profile-Kernel likelihood inference with diverging number of parameters
Generalized linear models varying coefficients high dimensionality asymptotic normality profile likelihood generalized likelihood ratio tests
2010/4/26
The generalized varying coefficient partially linear model with growing
number of predictors arises in many contemporary scientific endeavor. In
this paper we set foot on both theoretical and practi...
Large and moderate deviations principles for kernel estimators of the multivariate regression
Nadaraya-Watson estimator Recursive kernel estimator Large deviations principle Moderatedeviations principle
2010/4/27
In this paper, we prove large deviations principle for the
Nadaraya-Watson estimator and for the semi-recursive kernel
estimator of the regression in the multidimensional case.
Under suitable condi...