TAILIEUCHUNG - Báo cáo khoa hoc:" Estimating covariance functions for longitudinal data using a random regression model"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Estimating covariance functions for longitudinal data using a random regression model | 221 Genet. Sei. Evol. 30 1998 221-240 Inra Elsevier Paris Original article Estimating covariance functions for longitudinal data using a random regression model Karin Meyer Institute of Cell Animal and Population Biology Edinburgh University West Mains Road Edinburgh EH9 3JT Scotland UK Received 13 August 1997 accepted 31 March 1998 Abstract - A method is described to estimate genetic and environmental covariance functions for traits measured repeatedly per individual along some continuous scale such as time directly from the data by restricted maximum likelihood. It relies on the equivalence of a covariance function and a random regression model. By regressing on random orthogonal polynomials of the continuous scale variable the coefficients of covariance functions can be estimated as the covariances among the regression coefficients. A parameterisation is described which allows the rank of estimated covariance matrices and functions to be restricted thus facilitating a highly parsimonious description of the covariance structure. The procedure and the type of results which can be obtained are illustrated with an application to mature weight records of beef cows. Inra Elsevier Paris covariance functions genetic parameters longitudinal data I restricted maximum likelihood I random regression model Résumé Estimation des fonctions de covariance de données en sequence à par-tir d un modèle à coefficients de regression aléatoires. On décrit une methode d estimation des fonctions de covariance génétique et non génétiques pour des carac-tères mesurés plusieurs fois par individu le long d une échelle continue comme le temps. Elie s appuie directement sur les données à partir du maximum de vraisem-blance restreint en considérant 1 équivalence entre fonction de covariance et modèle de regression aléatoire. Les coefficients figurant dans les fonctions de covariance peuvent être estimés comme des covariances entre les coefficients de regression des observations par rapport à .

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