Robust order selection of mixtures of regression models with random effects

2021 ◽  
Author(s):  
Luísa Novais ◽  
Susana Faria
Keyword(s):  
Random Effects ◽  
Regression Models ◽  
Order Selection ◽  
Selection Of

scholarly journals Gradient boosting for linear mixed models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Colin Griesbach ◽  
Benjamin Säfken ◽  
Elisabeth Waldmann
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Selection Procedure ◽  
Classification Theory ◽  
Gradient Boosting ◽  
Random Structure ◽  
Boosting Algorithm ◽  
The One ◽  
Biased Estimates ◽  
Selection Of

Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

2021 ◽  
Vol 213 ◽  
pp. 106676
Author(s):  
Saeed Mohammadiun ◽  
Guangji Hu ◽  
Abdorreza Alavi Gharahbagh ◽  
Reza Mirshahi ◽  
Jianbing Li ◽  
...  
Keyword(s):  
Decision Tree ◽  
Oil Spill ◽  
Regression Models ◽  
The Arctic ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Oil Spill Response ◽  
Response Method ◽  
Selection Of

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

scholarly journals Selection of a suitable data set and model for the genetic evaluation of the linear description of conformation and type description in Old Kladruber horses

2012 ◽  
Vol 55 (2) ◽  
pp. 105-112
Author(s):  
L. Vostrý ◽  
K. Mach ◽  
J. Přibyl
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Suitable Model ◽  
Residual Variance ◽  
Linear Type ◽  
Colour Variant ◽  
Repeated Evaluation ◽  
Estimation Of Genetic Parameters ◽  
Selection Of

Abstract. The objective of this paper was to select a suitable data subset and statistical model for the estimation of genetic parameters for 36 traits of the linear type in 977 Old Kladruber horses. Two subsets were tested to identify a suitable subset for analysis. One subset included repeated evaluation of certain individuals, whereas the other did not. The most suitable subset included repeated evaluation (n=1 390). The selection of a suitable model was made from 4 candidate models. These models comprised a number of random effects (direct individual effect and animal permanent environmental effect of the animal) and a number of fixed effects (colour variant, stud, colour variant × stud interaction, sex, age at description, year of birth, year of description). The model was selected based on the Akaike information criterion (AIC, Akaike 1974), residual variance and heritability coefficient. The model that included colour variant, stud, colour variant × stud interaction, sex, age at description, and year of description as fixed effects and direct individual and animal permanent environment as random effects was the most suitable model for the estimation of genetic parameters and for the subsequent estimation of breeding values.

Random Effects Probit and Logistic Regression Models for Three-Level Data

Biometrics ◽  
1997 ◽  
Vol 53 (4) ◽  
pp. 1527 ◽  
Author(s):  
Robert D. Gibbons ◽  
Donald Hedeker
Keyword(s):  
Logistic Regression ◽  
Random Effects ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Level Data

scholarly journals Genetic evaluation of growth traits in Nellore cattle through multi-trait and random regression models

2018 ◽  
Vol 63 (No. 6) ◽  
pp. 212-221 ◽  
Author(s):  
B.B. Teixeira ◽  
R.R. Mota ◽  
R.B. Lôbo ◽  
L.P. Silva ◽  
A.P. Souza Carneiro ◽  
...  
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Regression Models ◽  
Growth Traits ◽  
Growth Curves ◽  
Genetic Evaluation ◽  
Information Criteria ◽  
Random Regression ◽  
Nellore Cattle ◽  
Heritability Estimates

We aimed to evaluate different orders of fixed and random effects in random regression models (RRM) based on Legendre orthogonal polynomials as well as to verify the feasibility of these models to describe growth curves in Nellore cattle. The proposed RRM were also compared to multi-trait models (MTM). Variance components and genetic parameters estimates were performed via REML for all models. Twelve RRM were compared through Akaike (AIC) and Bayesian (BIC) information criteria. The model of order three for the fixed curve and four for all random effects (direct genetic, maternal genetic, permanent environment, and maternal permanent environment) fits best. Estimates of direct genetic, maternal genetic, maternal permanent environment, permanent environment, phenotypic and residual variances were similar between MTM and RRM. Heritability estimates were higher via RRM. We presented perspectives for the use of RRM for genetic evaluation of growth traits in Brazilian Nellore cattle. In general, moderate heritability estimates were obtained for the majority of studied traits when using RRM. Additionally, the precision of these estimates was higher when using RRM instead of MTM. However, concerns about the variance components estimates in advanced ages via Legendre polynomial must be taken into account in future studies.

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