Results 271 to 280 of about 2,028,100 (318)

A mixed model for regressions

Biometrika, 1969
This paper gives a self-contained analysis of a mixed model for regressions. The model differs from the ordinary covariance model as well as from the error components regression model considered by Mundlak (1963), Wallace & Hussain (1969) and some other writers.
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Comparison of Model Selection for Regression

Neural Computation, 2003
We discuss empirical comparison of analytical methods for model selection. Currently, there is no consensus on the best method for finite-sample estimation problems, even for the simple case of linear estimators. This article presents empirical comparisons between classical statistical methods—Akaike information criterion (AIC) and Bayesian ...
Vladimir Cherkassky, Yunqian Ma
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Nonlinear Regression Analysis of the Joint-Regression Model

Biometrics, 1997
Summary: The joint-regression model for two-way data assumes a linear relation between a continuous response and column effects. Standard methods for fitting the model condition on estimates of the column effects, but including column effects as covariates in the model results in a nonlinear estimation problem.
Ng, Meei Pyng, Grunwald, Gary K.
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Regression correlation coefficient for a Poisson regression model

Computational Statistics & Data Analysis, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Akihito Takahashi, Takeshi Kurosawa
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Weighted-exponential regression model: An alternative to the gamma regression model

International Journal of Modeling, Simulation, and Scientific Computing, 2019
In this study, weighted-exponential regression model is proposed for modeling the right-skewed response variable as an alternative to the gamma regression model. The maximum likelihood, method of moments, least-squares and weighted least-squares estimation methods are used to estimate unknown parameters of re-parametrized weighted-exponential ...
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Nonlinear regression: a hybrid model

Computers & Operations Research, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Regression and the Linear Model

1981
A key feature in most statistical analyses is a statistical model and it will be helpful to look at examples of some simple models, and then discuss some terminology.
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Regression and Hierarchical Regression Models

2012
Linear regression is the most commonly used statistical method for quantifying the relationships between variables and for using one or more variables to predict unobserved values of other variables. This chapter concerns Bayesian linear regression as well as more general multivariate methods—generalized linear models and hierarchical regression models.
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Regression Models for Quantiles

Journal of Mathematical Sciences, 2001
Blagoveshchenskii, Yu. N., Svyatodukh, I. V.   +1 more
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Mixture Regression Models

2002
Wedel, M., DeSarbo, W.S.
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