搜索结果: 16-30 共查到“理论统计学 kernel”相关记录32条 . 查询时间(0.109 秒)
Minimax properties of beta kernel density estimators
Beta Kernel Density Minimax estimation
2010/3/9
In this paper, we are interested in the study of beta kernel estimators from
an asymptotic minimax point of view. It is well known that beta kernel estimators
are—on the contrary of classical kernel...
Kernel Partial Least Squares is Universally Consistent
Kernel Partial Least Squares Universally Consistent
2010/3/18
We prove the statistical consistency of kernel Partial Least Squares
Regression applied to a bounded regression learning problem on a re-
producing kernel Hilbert space. Partial Least Squares stands...
Regularization in kernel learning
Regression reproducing kernel Hilbert space regulation leastsquares model selection
2010/3/9
Under mild assumptions on the kernel, we obtain the best known
error rates in a regularized learning scenario taking place in the corresponding
reproducing kernel Hilbert space (RKHS). The main nove...
Optimal Sequential Kernel Detection for Dependent Processes
Enzyme kinetics financial econometrics nonparametric regression statis-tical genetics quality control
2010/3/9
In many applications one is interested to detect certain (known) patterns in
the mean of a process with smallest delay. Using an asymptotic framework which allows
to capture that feature, we study a...
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。
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...
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...