Results 251 to 260 of about 427,529 (281)

Stochastic and non-stochastic covariates in binary regression

2020
BAĞIMSIZ DEĞİŞKENLERİN S TO KASTİ K VE STOKASTİK OLMADIĞI DURUMLARDA İKİLİ REGRESYON Evrim Oral Hacettepe Üniversitesi, İstatistik Bölümü, İstatistik Teorisi Anabilim Dalı ÖZ Olabilirlik denklemlerinin çözümleri genellikle sorunludur, dolayısıyla da en çok olabilirlik tahmin edicilerinin elde edilmesi zordur. 1967 yılında M. L. Tiku, bu zorluğu ortadan
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Stochastic Approximation and NonLinear Regression

2003
This monograph addresses the problem of "real-time" curve fitting in the presence of noise, from the computational and statistical viewpoints. It examines the problem of nonlinear regression, where observations are made on a time series whose mean-value function is known except for a vector parameter.
Arthur E. Albert, Leland A. Gardner
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Stochastic DCA for Sparse Multiclass Logistic Regression

2017
In this paper, we deal with the multiclass logistic regression problem, one of the most popular supervised classification method. We aim at developing an efficient method to solve this problem for large-scale datasets, i.e. large number of features and large number of instances.
Hoai An Le Thi, Hoai Minh Le, Duy Nhat Phan, Bach Tran   +3 more
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Stochastic Approximation and Nonlinear Regression

Technometrics, 1969
W. T. Federer, A. E Albert, L. A. Gardner   +2 more
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Stochastic Approximation with Gaussian Process Regression

2021 Winter Simulation Conference (WSC), 2021
Yingcui Yan, Haihui Shen, Zhibin Jiang
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Stochastic Parameter Regression Model

Journal of Marketing Research, 1986
Eric R. Ziegel, P. Newbold, T. Bos
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Stochastic differential equations: singularity of coefficients, regression models, and stochastic approximation

Russian Mathematical Surveys, 1996
This survey article is devoted to the application of the stochastic calculus for semimartingales to statistical approximation and models of financial mathematics. Correspondingly to this aim the article is divided into four parts. The first part contains the limit theorems for the continuous time martingales.
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Regularized multivariate stochastic regression

2018
In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves ...
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Stochastic Development Regression Using Method of Moments

2017
This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using the stochastic development construction.
Line Kühnel, Stefan Sommer
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Regression-based orderings and measures of stochastic depence

Series Statistics, 1981
General axiomatic approach to the so-called global dependence of a random variable xon a random vector Y= Y t,Y n) is proposed. natural orderings and measures of global dependence are discussed and examplified by some real and function-valued measures of dependence.
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