Fitting genetic models with LISREL: Hypothesis testing

M. C. Neale; A. C. Heath; J. K. Hewitt; L. J. Eaves; D. W. Fulker

doi:10.1007/BF01065882

Fitting genetic models with LISREL: Hypothesis testing

M. C. Neale, A. C. Heath, J. K. Hewitt, L. J. Eaves, D. W. Fulker

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Abstract

A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.

Original language	English
Pages (from-to)	37-49
Number of pages	13
Journal	Behavior genetics
Volume	19
Issue number	1
DOIs	https://doi.org/10.1007/BF01065882
State	Published - Jan 1989

Keywords

LISREL
maximum likelihood
structural modeling

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10.1007/BF01065882

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abstract = "A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.",

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author = "Neale, {M. C.} and Heath, {A. C.} and Hewitt, {J. K.} and Eaves, {L. J.} and Fulker, {D. W.}",

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AU - Hewitt, J. K.

AU - Eaves, L. J.

AU - Fulker, D. W.

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AB - A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.

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KW - maximum likelihood

KW - structural modeling

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ER -

Fitting genetic models with LISREL: Hypothesis testing

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Keywords

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