References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt145",{id:"formSmash:upper:j_idt145",widgetVar:"widget_formSmash_upper_j_idt145",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt146_j_idt148",{id:"formSmash:upper:j_idt146:j_idt148",widgetVar:"widget_formSmash_upper_j_idt146_j_idt148",target:"formSmash:upper:j_idt146:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

Likelihood ratio tests in behavioral genetics: Problems and solutionsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true}); PrimeFaces.cw("SelectBooleanButton","widget_formSmash_j_idt185",{id:"formSmash:j_idt185",widgetVar:"widget_formSmash_j_idt185",onLabel:"Hide others and affiliations",offLabel:"Show others and affiliations"});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2006 (English)In: Behavior Genetics, ISSN 0001-8244, E-ISSN 1573-3297, Behavior Genetics, ISSN 0001-8244, Vol. 36, no 2, 331-340 p.Article in journal (Refereed) Published
##### Abstract [en]

##### Place, publisher, year, edition, pages

Springer , 2006. Vol. 36, no 2, 331-340 p.
##### Keyword [en]

Boundary parameter; chi-square distribution; likelihood ratio test; twin model; variance
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:su:diva-25716DOI: 10.1007/s10519-005-9034-7OAI: oai:DiVA.org:su-25716DiVA: diva2:200342
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##### Note

Part of urn:nbn:se:su:diva-848Available from: 2006-03-02 Created: 2006-03-02 Last updated: 2010-12-14Bibliographically approved
##### In thesis

The likelihood ratio test of nested models for family data plays an important role in the assessment of genetic and environmental influences on the variation in traits. The test is routinely based on the assumption that the test statistic follows a chi-square distribution under the null, with the number of restricted parameters as degrees of freedom. However, tests of variance components constrained to be non-negative correspond to tests of parameters on the boundary of the parameter space. In this situation the standard test procedure provides too large

p-values and the use of the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for model selection is problematic. Focusing on the classical ACE twin model for univariate traits, we adapt existing theory to show that the asymptotic distribution for the likelihood ratio statistic is a mixture of chi-square distributions, and we derive the mixing probabilities. We conclude that when testing the AE or the CE model against the ACE model, the p-values obtained from using the v2 (1 df) as the reference distribution should be halved. When the E model is tested against the ACE model, a mixture of v2(0 df), v2(1 df) and v2 (2 df) should be used as the reference distribution, and we provide a simple formula to compute the mixing probabilities. Similar results for tests of the AE, DE and E models against the ADE model are also derived. Failing to use the appropriate reference distribution can lead to invalid conclusions.

1. Latent variable models for longitudinal twin data$(function(){PrimeFaces.cw("OverlayPanel","overlay200343",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay200343",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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