Formulation of Fuzzy Random Regression Model

2011 ◽  
pp. 1-20
Author(s):  
Junzo Watada ◽  
Shuming Wang ◽  
Witold Pedrycz
Keyword(s):  
Regression Model ◽  
Random Regression ◽  
Random Regression Model ◽  
Fuzzy Random

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 Test-day or 305-day milk yield for genetic evaluation of Gir cattle

2019 ◽  
Vol 54 ◽  
Author(s):  
Rodrigo Junqueira Pereira ◽  
Denise Rocha Ayres ◽  
Mário Luiz Santana Junior ◽  
Lenira El Faro ◽  
Aníbal Eugênio Vercesi Filho ◽  
...  
Keyword(s):  
Regression Model ◽  
Milk Yield ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Breeding Values ◽  
Random Regression Model ◽  
The Mean ◽  
Yield Record ◽  
Genetic Evaluations ◽  
Dairy Breed

Abstract: The objective of this work was to compare genetic evaluations of milk yield in the Gir breed, in terms of breeding values and their accuracy, using a random regression model applied to test-day records or the traditional model (TM) applied to estimates of 305-day milk yield, as well as to predict genetic trends for parameters of interest. A total of 10,576 first lactations, corresponding to 81,135 test-day (TD) records, were used. Rank correlations between the breeding values (EBVs) predicted with the two models were 0.96. The percentage of animals selected in common was 67 or 82%, respectively, when 1 or 5% of bulls were chosen, according to EBVs from random regression model (RRM) or TM genetic evaluations. Average gains in accuracy of 2.7, 3.0, and 2.6% were observed for all animals, cows with yield record, and bulls (sires of cows with yield record), respectively, when the RRM was used. The mean annual genetic gain for 305-day milk yield was 56 kg after 1993. However, lower increases in the average EBVs were observed for the second regression coefficient, related to persistency. The RRM applied to TD records is efficient for the genetic evaluation of milk yield in the Gir dairy breed.

scholarly journals Analysis of longitudinal data of Nellore cattle from performance test at pasture using random regression model

SpringerPlus ◽  
2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Fernando Brito Lopes ◽  
Cláudio Ulhôa Magnabosco ◽  
Fernanda Paulini ◽  
Marcelo Corrêa da Silva ◽  
Eliane Sayuri Miyagi ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Performance Test ◽  
Random Regression ◽  
Random Regression Model ◽  
Nellore Cattle

The Validity of Polynomial Regression in the Random Regression Model

1978 ◽  
Vol 48 (4) ◽  
pp. 511-515 ◽  
Author(s):  
Elliot M. Cramer ◽  
Mark I. Appelbaum
Keyword(s):  
Regression Model ◽  
Polynomial Regression ◽  
Random Regression ◽  
Random Regression Model

scholarly journals A Bayesian generalized random regression model for estimating heritability using overdispersed count data

2015 ◽  
Vol 47 (1) ◽  
Author(s):  
Colette Mair ◽  
Michael Stear ◽  
Paul Johnson ◽  
Matthew Denwood ◽  
Joaquin Prada Jimenez de Cisneros ◽  
...  
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Random Regression ◽  
Random Regression Model
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