TAILIEUCHUNG - Báo cáo sinh học: " Statistical analysis of ordered categorical data via a structural heteroskedastic threshold model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Statistical analysis of ordered categorical data via a structural heteroskedastic threshold model | Genet Sei Evol 1996 28 249-273 Elsevier INRA 249 Original article Statistical analysis of ordered categorical data via a structural heteroskedastic threshold model JL Foulley1 D Gianola2 1 Station de génétique quantitative et appliquée Institut national de la recherche agronomique centre de recherches de Jouy 78352 Jouy-en-Josas cedex France 2 Department of Meat and Animal Science University of Wisconsin-Madison Madison WI 53706 USA Received 2 October 1995 accepted 25 March 1996 Summary - In the standard threshold model differences among statistical subpopulations in the distribution of ordered polychotomous responses are modeled via differences in location parameters of an underlying normal scale. A new model is proposed whereby subpopulations can also differ in dispersion scaling parameters. Heterogeneity in such parameters is described using a structural linear model and a loglink function involving continuous or discrete covariates. Inference estimation testing procedures goodness of fit about parameters in fixed-effects models is based on likelihood procedures. Bayesian techniques are also described to deal with mixed-effects model structures. An application to calving ease scores in the US Simmental breed is presented the heteroskedastic threshold model had a better goodness of fit than the standard one. threshold character heteroskedasticity maximum likelihood mixed linear model calving difficulty Resume Analyse statistique de variables discretes ordonnées par un modèle à seuils hétéroscédastique. Dans le modèle à seuils classique les differences de réponses entre sous-populations selon des categories discretes ordonnées sont modélisées par des differences entre paramètres de position mesurés sur une variable normale sous-jacente. L approche presentee id suppose que ces sous-populations different aussi par leurs paramètres de dispersion ou paramètres d echelle . L hétérogénéité de ces paramètres est décrite par un modèle linéaire structurel et une fonction .

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