tailieunhanh - Báo cáo sinh học: "Inference for threshold models with variance components from the generalized linear mixed model perspective"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Inference for threshold models with variance components from the generalized linear mixed model perspective | Genet Sei Evol 1995 27 15-32 Elsevier INRA 15 Original article Inference for threshold models with variance components from the generalized linear mixed model perspective B Engel1 w Buist2 A Visscher2 1 DLO Agricultural Mathematics Group GLW-DLO PO Box 100 6700 AC Wageningen 2 DLO Institute for Animal Science and Health ID-DLO PO Box 501 3700 AM Zeist The Netherlands Received 10 December 1993 accepted 5 October 1994 Summary - The analysis of threshold models with fixed and random effects and associated variance components is discussed from the perspective of generalized linear mixed models GLMMs . Parameters are estimated by an interative procedure referred to as iterated re-weighted REML IRREML . This procedure is an extension of the iterative re-weighted least squares algorithm for generalized linear models. An advantage of this approach is that it immediately suggests how to extend ordinary mixed-model methodology to GLMMs. This is illustrated for lambing difficulty data. IRREML can be implemented with standard software available for ordinary normal data mixed models. The connection with other estimation procedures eg the maximum a posteriori MAP approach is discussed. A comparison by simulation with a related approach shows a distinct pattern of the bias of MAP and IRREML for heritability. When the number of fixed effects is reduced while the total number of observations is kept about the same bias decreases from a large positive to a large negative value seemingly independently of the sizes of the fixed effects. binomial data threshold model variance components generalized linear model restricted maximum likelihood Resume Inference sur les composantes de variance des modèles à seuil dans une perspective de modèle linéaire mixte generalise. L analyse des modèles à seuils avec effets fixes et aléatoires et des composantes de variance correspondantes est id placée dans la perspective des modèles linéaires mixtes generalises GLMMs . Les paramètres sont estimés par