Results 241 to 250 of about 425,331 (287)

Switching regression metamodels in stochastic simulation

European Journal of Operational Research, 2016
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Reis dos Santos, M. Isabel, Reis dos Santos, Pedro M.   +1 more
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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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Binary Regression with Stochastic Covariates

Communications in Statistics - Theory and Methods, 2006
In binary regression the risk factor X has been treated in the literature as a non-stochastic variable. In most situations, however, X is stochastic. We present solutions applicable to such situations. We show that our solutions are more precise than those obtained by treating X as non-stochastic when, in fact, it is stochastic.
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Stochastic semiparametric regression for spectrum cartography

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
An online spectrum cartography algorithm is proposed to reconstruct power spectral density (PSD) maps in space and frequency based on compressed and quantized sensor measurements. The emerging regression task is addressed by decomposing the PSD at every location into a linear combination of the power spectra (due to individual transmitters and ...
Daniel Romero, Seung-Jun Kim, Georgios B. Giannakis   +2 more
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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 regression model with heteroscedastic disturbance

Annals of the Institute of Statistical Mathematics, 1995
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Chang, Der-Shin, Lin, Guan-Chyun
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Stochastic covariates in binary regression

2004
Summary: Binary regression has many medical applications. In applying the technique, the tradition is to assume the risk factor \(X\) as a non-stochastic variable. In most situations, however, \(X\) is stochastic. In this study, we discuss the case when \(X\) is stochastic in nature, which is more realistic from a practical point of view than \(X ...
ORAL, Evrim, GÜNAY, Süleyman
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Estimation of Stationary Stochastic Regression Parameters

Journal of the American Statistical Association, 1970
Abstract This article considers repeated regression experiments wherein the regression parameters vary according to a stationary stochastic process with known covariance structure. Expressions are derived for best linear estimators and predictors of linear functions of the regression parameters.
Thomas D. Burnett, Donald Guthrie
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Regression Methods for Stochastic Control Problems

SSRN Electronic Journal, 2008
In this paper we develop several regression algorithms for solving general stochastic optimal control problems via Monte Carlo. This type of algorithms is particularly useful for problems with high-dimensional state space and complex dependence structure of the underlying Markov process with respect to some control.
Denis Belomestny, Anastasia Kolodko, John Schoenmakers   +2 more
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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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