Results 191 to 200 of about 3,604,166 (239)
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The finite polygenic mixed model: An alternative formulation for the mixed model of inheritance
Theoretical and Applied Genetics, 1994This paper presents a mixed model of inheritance with a finite number of polygenic loci. This model leads to a likelihood that can be calculated using efficient algorithms developed for oligogenic models. For comparison, likelihood profiles were obtained for the finite polygenic mixed model, the usual mixed model, with exact and approximate ...
Rohan L Fernando, Robert C Elston
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Improvement of mixed predictors in linear mixed models
Journal of Applied Statistics, 2020In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean ...
Özge Kuran, M. Revan Özkale
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A Shell Model for Optimal Mixing
Journal of Nonlinear Science, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christopher J. Miles, Charles R. Doering
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2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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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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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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Computational Statistics, 2003
The author gives a brief overview of general design mixed models (MM) with emphasis on the use of the mixed model framework to fit and make inference for a wide variety of semiparametric regression models. It is demonstrated that the MM approach to smoothing has several advantages.
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The author gives a brief overview of general design mixed models (MM) with emphasis on the use of the mixed model framework to fit and make inference for a wide variety of semiparametric regression models. It is demonstrated that the MM approach to smoothing has several advantages.
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Functional Mixed Effects Models
Biometrics, 2002Summary.In this article, a new class of functional models in which smoothing splines are used to model fixed effects as well as random effects is introduced. The linear mixed effects models are extended to non‐parametric mixed effects models by introducing functional random effects, which are modeled as realizations of zero‐mean stochastic processes ...
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2010
The Context-Tree-Weighting algorithm is an example of an computationally efficient way to compute a Bayesian mixture of context-tree models. In this talk I will present several classes of models for which efficient mixing procedures exist. After briefly touching the binary CTW method I will discuss an efficient way to determine the MAP context tree ...
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The Context-Tree-Weighting algorithm is an example of an computationally efficient way to compute a Bayesian mixture of context-tree models. In this talk I will present several classes of models for which efficient mixing procedures exist. After briefly touching the binary CTW method I will discuss an efficient way to determine the MAP context tree ...
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