搜索结果: 1-11 共查到“理论统计学 Gaussian processes”相关记录11条 . 查询时间(0.127 秒)
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/20
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
A Novel Exact Representation of Stationary Colored Gaussian Processes (Fractional Differential Approach)
Digital Filtering Filtered White Noises Power Spectral Density Fractional Brownian Motion Fractional Stochastic Differential Equation Fractional Spectral Moments
2013/4/28
A novel representation of functions, called generalized Taylor form, is applied to the filtering of white noise processes. It is shown that every Gaussian colored noise can be expressed as the output ...
Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes
Hermite polynomials of a Gaussian process long–memory parameter non–Gaussian Rosenblatt
2011/6/16
We consider stationary processes with long memory which are non–Gaussian and represented
as Hermite polynomials of a Gaussian process. We focus on the corresponding
wavelet coefficients and study th...
Predictive Active Set Selection Methods for Gaussian Processes
Gaussian process classifi cation active set selection predictive distribution expectation propagation
2011/3/24
We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists on a two step alter...
On stochastic equations for the class of Gaussian processes
stochastic equations the class of Gaussian processes
2009/9/24
Ito stochastic equations are derived for a class of
multidimensional Gaussian processes appearing in connection with
generalized spline functions. Some analytic consequences for the
spline interpol...
On the supremum from Gaussian processes over infinite horizon
the supremum from Gaussian processes infinite horizon
2009/9/22
In the paper we study the asymptotic of the tail of
distribution function P(A(X, c) > x) for x + m, where A(X, c) is the
supremum of X(t)-ct over [0, co). In particular, X(t) is the fractional
Brow...
Level crossing and local time for regularized Gaussian processes
Level crossing local time regularized Gaussian processes
2009/9/22
Let (X,,t~ [0, 11) be a oentred stationary Gaussian
process defined on (D,A , P) with covariance function satisfying
Define the regularized process
X' = cp, * X and YE = Xc/oe, where CT~ = var Xf ,...
Characterizations of polynomial-Gaussian processes that are Markovian
Characterizations polynomial-Gaussian processes Markovian
2009/9/21
We consider questions of characterizing a stochastic
process X = (X,,t 2 0) by the properties of the first two conditional
moments. Our first result is a new version of the classical P. Levy
charac...
Level crossings and other level functionals of stationary Gaussian processes
Gaussian processes/fields Hermite polynomials level curve level functionals local time (factorial) moments
2009/5/18
This paper presents a synthesis on the mathematical work done on level crossings of stationary Gaussian processes, with some extensions. The main results [(factorial) moments, representation into the ...
A remark on the equivalence of Gaussian processes
Gaussian processes classical equivalence result
2009/3/19
In this note we extend a classical equivalence result for Gaussian stationary processes to the more general setting of Gaussian processes with stationary increments. This will allow us to apply it in ...
Adaptive wavelet based estimator of the memory parameter for stationary Gaussian processes
Adaptive wavelet estimator memory parameter stationary Gaussian processes
2010/4/26
This work is intended as a contribution to a wavelet-based adaptive estimator of the memory parameter in the classical semi-parametric framework for Gaussian stationary processes. In particular we int...