Maximum Likelihood, Restricted Maximum Likelihood, and Bayesian Estimation for Mixed Models

2016 ◽  
pp. 51-58
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Mixed Models ◽  
Restricted Maximum Likelihood

Comparison of different methods for fitting nonlinear mixed forest models and for making predictions

2011 ◽  
Vol 41 (8) ◽  
pp. 1671-1686 ◽  
Author(s):  
Yuqing Yang ◽  
Shongming Huang
Keyword(s):  
Maximum Likelihood ◽  
Conditional Expectation ◽  
Mixed Models ◽  
Mixed Forest ◽  
Restricted Maximum Likelihood ◽  
Order Method ◽  
First Order ◽  
Nonlinear Mixed Models ◽  
Model Predictions ◽  
First Order Method

Nonlinear mixed models have become popular in forestry applications, and various methods have been proposed for fitting such models. However, it is difficult or even confusing to choose which method to use, and there is not much relevant information available, especially in the forestry context. The main objective of this study was to compare three commonly used methods for fitting nonlinear mixed models: the first-order, the first-order conditional expectation, and the adaptive Gaussian quadrature methods. Both the maximum likelihood and restricted maximum likelihood parameter estimation techniques were evaluated. Three types of data common in forestry were used for model fitting and model application. It was found that the first-order conditional expectation method provided more accurate and precise predictions for two models developed from data with more observations per subject. For one model developed on data with fewer observations per subject, the first-order method provided better model predictions. All three models fitted by the first-order method produced some biologically unrealistic predictions, and the problem was more obvious on the data with fewer observations per subject. For all three models fitted by the first-order and first-order conditional expectation methods, the maximum likelihood and restricted maximum likelihood fits and the resulting model predictions were very close.

scholarly journals Sugarcane Family Selection and Genetic Parameter Prediction via the REML/BLUP Methodology

2019 ◽  
Vol 11 (11) ◽  
pp. 303
Author(s):  
Hugo Zeni Neto ◽  
Renato Frederico dos Santos ◽  
Luiz Gustavo da Mata Borsuk ◽  
Henrique Sanches Angeli ◽  
Guilherme Souza Berton
Keyword(s):  
Maximum Likelihood ◽  
Mixed Models ◽  
Genetic Parameter ◽  
Restricted Maximum Likelihood ◽  
Family Selection ◽  
Improvement Program ◽  
Breeding Programs ◽  
Program Selection ◽  
The Family ◽  
Parameter Prediction

Most sugarcane breeding programs tend to evaluate low heritability characteristics during the initial stages of genotype selection. Thus, family selection has been recently preferred. In this context, the aim of the present study was to select the best family among 78 sugarcane families, as well as estimate genetic values through the mixed models of restricted-maximum likelihood and best non-bias predictor (REML/BLUP) methodology, originating from the República Brasil 2005 (RB05) series. This strategy was deemed efficient, and 34 to 38 families were chosen from four evaluated characteristics underexplored by genetic researchers such as total plot mass (MTT), mean mass of one tiller in the plot (M1C), stature (EST), and mean number of canes per square meter (NCM). The family increments ranging from 6.02 to 82.11%, in the next genetic culture improvement program selection phases.

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