Results 11 to 20 of about 83,537 (273)
Nested Generalized Linear Mixed Models: An Orthodox Best Linear Unbiased Predictor Approach
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2007 SummaryWe introduce a new class of generalized linear mixed models based on the Tweedie exponential dispersion model distributions, accommodating a wide range of discrete, continuous and mixed data. Using the best linear unbiased predictor of random effects, we obtain an optimal estimating function for the regression parameters in the sense of Godambe,
Jørgensen, Bent, Ma, Renjun
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Journal of Dairy Science, 2022 : Genomic evaluation based on a single-step model uses all available data of phenotype, genotype, and pedigree; therefore, it should provide unbiased genomic breeding values with a higher correlation of prediction than the current multistep genomic model.
H. Alkhoder +3 more
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An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP) [PDF]
Chilean journal of agricultural research, 2016 A major obstacle in corn (Zea mays L.) breeding programs is how to obtain strains which, when combined, ensure increased productivity in all crop characteristics. Therefore, the objective of this study was to evaluate the prediction efficiency of hybrid maize using diallel analysis and the best linear unbiased predictor (BLUP).
Oliveira, Gustavo H. F. +6 more
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An Asymmetric Area Model-Based Approach for Small Area Estimation Applied to Survey Data
Revstat Statistical Journal, 2021 The Birnbaum–Saunders distribution is asymmetrical and has received considerable attention due to its properties and its relationship with the normal distribution. In this paper, we propose a methodology for estimating the mean of small areas based on a
Marcelo Rodríguez +5 more
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Empirical best linear unbiased predictors in multivariate nested-error regression models [PDF]
Communications in Statistics - Theory and Methods, 2019 For analyzing unit-level multivariate data in small area estimation, we consider the multivariate nested error regression model (MNER) and provide the empirical best linear unbiased predictor (EBLUP) of a small area characteristic based on second-order unbiased and consistent estimators of the `within' and `between' multivariate components of variance.
Ito, Tsubasa, Kubokawa, Tatsuya
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Journal of Dairy Science, 2021 Genetic groups, also called unknown or phantom parents groups, are often used in dairy cattle genetic evaluations to account for selection that cannot be accounted for by known genetic relationships. With the advent of genomic evaluations, the theory of genetic groups was extended to the so-called single-step genomic BLUP (ssGBLUP).
Jeremie Vandenplas +2 more
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On Generalized Upper(k)Record Values From Weibull Distribution
Statistica, 2015 In this paper we study the generalized upper(k)record values arising from Weibull distribution. Expressions for the moments and product moments of those generalized upper(k)record values are derived.
Jerin Paul, Poruthiyudian Yageen Thomas
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On empirical best linear unbiased predictor under a Linear Mixed Model with correlated random effects [PDF]
Econometrics, 2020 The problem of small area prediction is considered under a Linear Mixed Model. The article presents a proposal of an empirical best linear unbiased predictor under a model with two correlated random effects. The main aim of the simulation analyses is a study of an influence of the occurrence of a correlation between random effects on properties of the ...
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REVISTA BRASILEIRA DE BIOMETRIA, 2021 We develop best linear unbiased predictors (BLUP) of the latent values of labeled sample units selected from a finite population when there are two distinct sources of measurement error: endogenous, exogenous or both. Usual target parameters are the population mean, the latent values associated to a labeled unit or the latent value of the unit that ...
German MORENO +2 more
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Henderson's method approach to Kernel prediction in partially linear mixed models
Cumhuriyet Science Journal, 2020 In this article, we propose Kernel prediction in partially linear mixed models by using Henderson's method approach. We derive the Kernel estimator and the Kernel predictor via the mixed model equations (MMEs) of Henderson's that they give the best ...
Seçil Yalaz, Özge Kuran
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