Let (Y1,θ1), . . . ,(Yn,θn) be independent real-valued random vectors with Yi, given θi, is distributed according to a distribution depending only on θi for i= 1, . . . ,n. In this paper, best linear unbiased predictors (BLUPs) of the θi’s are investigated. We show that BLUPs of θi’s do not exist in certain situations. Furthermore, we present a general empirical Bayes technique for deriving BLUPs.

This paper presents methods to provide an optimal evaluation of the nuclear masses. The techniques used for this purpose come from data assimilation (DA) that allows combining, in an optimal and consistent way, information coming from experiment and from numerical modelling. Using all the available information, it leads to improve not only masses evaluations, but also their uncertainties. Each ...

Many traits of economic importance are controlled by a large number of genes (polygenes) acting in concert. Selection on estimated breeding values (EBVs) based on the infinitesimal model using Best Linear Unbiased Prediction (BLUP) has proven to be very effective for traits that are easily measured. The infinitesimal model assumes that there are an infinite number of genes, each having a small ...

Consider the small area estimation when positive area-level data like income, revenue, harvests or production are available. Although a conventional method is the logtransformed Fay-Herriot model, the log-transformation is not necessarily appropriate. Another popular method is the Box-Cox transformation, but it has drawbacks that the maximum likelihood estimator (ML) of the transformation param...

Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR), which is equivalent to best linear unbiased prediction (BLUP) when the genetic covariance betwe...

We constraint on computer the best linear unbiased generalized statistics of random field for the best linear unbiased generalized statistics of an unknown constant mean of random field and derive the numerical generalized least-squares estimator of an unknown constant mean of random field. We derive the third constraint of spatial statistics and show that the classic generalized least-squares ...

Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, expl...