Abstract: The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. experiment carried out 2002 2008 and in 2010 municipality Rio Branco, state Acre, Brazil. Analyzes deviance were performed t...

Total litter weight weaned at 120 d postpartum per ewe lambing is often believed to be a measure of range ewe productivity. Genetic correlations for litter weight weaned at 120 d with prolificacy, growth, and wool traits for Columbia, Polypay, Rambouillet, and Targhee sheep were estimated using REML with animal models. Observations per breed ranged from 5,140 to 7,083 for litter weight weaned, ...

Amultilevelmodel for ordinal data in generalized linearmixedmodels (GLMM) framework is developed to account for the inherent dependencies among observationswithin clusters. Motivated by a data set from the British Social Attitudes Panel Survey (BSAPS), the random district effects and respondent effects are incorporated into the linear predictor to accommodate the nested clusterings. The fixed (...

Introduction Multivariate Fay–Herriot models for estimating small area indicators are introduced. Among the available procedures for fitting linear mixed models, the residual maximum likelihood (REML) is employed. The empirical best predictor (EBLUP) of the vector of area means is derived. An approximation to the matrix of mean squared crossed prediction errors (MSE) is given and four MSE estim...

The objectives of this study were to estimate genetic parameters and sire breeding values for average daily gain (ADG) and carcass traits using sire-maternal grandsire model with REML approach, sire model with REML approach, sire model without relationships among sires and with REML and ANOVA approach, and to investigate advantages and disadvantages of these methods. Data were collected from 42...

With the recent advances in graph neural networks, there is a rising number of studies on graph-based multi-label classification with consideration object dependencies within visual data. Nevertheless, representations can become indistinguishable due to complex nature label relationships. We propose image framework based transformer networks fully exploit inter-label interactions. The paper pre...