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A MULTIVARIATE NORMAL MODEL FOR PEDIGREE AND LONGITUDINAL DATA AND THE SOFTWARE ‘FISHER’
Author(s) -
Hopper John L.,
Mathews John D.
Publication year - 1994
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1994.tb00859.x
Subject(s) - multivariate statistics , statistics , inference , restricted maximum likelihood , mathematics , econometrics , computer science , maximum likelihood , artificial intelligence
Summary In 1918 R.A. Fisher published an interpretation of covariation between relatives in terms of Mendelian inheritance, which has allowed inference on genetic and environmental components of variation from plant, animal and human pedigree data. Fisher had introduced maximum likelihood six years earlier. His 1918 paper abo contained the basics of linear regression and decomposition of variance. These concepts have now been united to allow flexible modelling of the mean and covariance structure of non‐independent data on continuous traits, using maximum likelihood under a multivariate normal assumption. FISHER is a software package, designed for pedigree analysis and easily adapted for repeated measures and longitudinal data analysis. A range of applications illustrate FISHER as a useful statistical tool. Issues related to assumptions, tests‐of‐fit, and robustness of inference are discussed.