Maximum Likelihood and Bayesian Estimation of QTL Parameters with Random Effects Included in the Model

2016 ◽  
pp. 43-50
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Random Effects

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

A note on heritability estimates for growth traits in male and female Romanov sheep

1993 ◽  
Vol 57 (2) ◽  
pp. 326-328 ◽  
Author(s):  
G. A. María ◽  
K. G. Boldman ◽  
L. D. van Vleck
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Effect ◽  
Male And Female ◽  
Males And Females ◽  
Direct Genetic Effect

A total of 1855 records were analysed using restricted maximum likelihood (REML) techniques to estimate heritabilities separately for males and females lambs on birth weight (BW), weaning weight (WW), 90-day weight (W90) and average daily gains birth to weaning (Cl) and weaning to 90 days (C2). An animal model including fixed effects of year × season, parity, litter size and rearing type; and random effects of direct genetic effect (h2D) and residual was applied. Estimates ofh2Dfor BWwere 048 (males) and 0·50 (females); for WW 0·35 (males) and 0·22 (females); for W90 0·21 (males) and 0·31 (females); for Cl 0·20 (males) and 0·25 (females); and for C2 0·18 (males) and 0·29 (females).

scholarly journals Statistical Inference for the Weibull Distribution Based on δ-Record Data

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Raúl Gouet ◽  
F. Javier López ◽  
Lina Maldonado ◽  
Gerardo Sanz
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Statistical Inference ◽  
Numerical Computation ◽  
Weibull Model ◽  
Rainfall Series ◽  
Estimation Of Parameters ◽  
Record Data

We consider the maximum likelihood and Bayesian estimation of parameters and prediction of future records of the Weibull distribution from δ -record data, which consists of records and near-records. We discuss existence, consistency and numerical computation of estimators and predictors. The performance of the proposed methodology is assessed by Montecarlo simulations and the analysis of monthly rainfall series. Our conclusion is that inferences for the Weibull model, based on δ -record data, clearly improve inferences based solely on records. This methodology can be recommended, more so as near-records can be collected along with records, keeping essentially the same experimental design.

scholarly journals The algorithm of Bayesian estimation of maternal genetic and permanent maternal environmental variances in a two-trait binary threshold model

2011 ◽  
Vol 49 (No. 2) ◽  
pp. 58-63
Author(s):  
E. Skotarczak ◽  
M. Szyd ◽  
A. Dobek ◽  
K. Moli ◽  
T. Szwaczkowski
Keyword(s):  
Bayesian Estimation ◽  
Gibbs Sampling ◽  
Random Effects ◽  
Environmental Effects ◽  
Fixed Effects ◽  
Threshold Model ◽  
Sampling Procedure ◽  
Numerical Example ◽  
Estimation And Prediction

The paper presents an algorithm for the estimation and prediction of parameters in a two-trait binary threshold model. The model includes fixed effects and the following random effects: genetic direct additive, genetic maternal additive and permanent maternal environmental effects. The Gibbs sampling procedure was used to estimate the parameters. The algorithm was illustrated with a numerical example showing appropriateness of the proposed method.  

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