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Modeling Uncertainty in Latent Class Membership a Case Study in Criminology ;Daniel S. Nagin

文献名称 Modeling Uncertainty in Latent Class Membership a Case Study in Criminology ;Daniel S. Nagin
文献作者 Kathryn Roeder ;Kevin G. Lynch
作者所在单位 Department of Statistics ;Western Psychiatric Institute and Clinic ;The Heinz School
文献分类 已发表文献
学科一级分类 统计
学科二级分类 统计学
文献摘要 Social scientists are commonly interested in relating a latent trait(e.g., criminal tendency) to measurable
individual covariates(e.g., poor parenting)to understand what dedines or perhaps causes the latent trait. In
this article we develop an efficient and convenient method for answering such questions. The basic model
presumes that two types of variables have been measured-response variables(possibly longitudinal)that
partially determine the latent class membership and covariates or risk factors that we wish to relate to these
latent class variables. The model assumes that these observable variables are conditionally independent,
given the latent class variable. We use a mixture model for the joint distribution of the observables. We apply
this model to longitudinal data set assembled as part of the Cambridge Study of Delinquent Development to
test a fundamental theory of criminal development. This theory holds that crime is committed by two distinct
groups within the population-adolescent limited offenders and life course persistent offenders. As these
labels suggest, the two groups are distinguished by the longevity of their offending careers. The theory also
predicts that life course persistent offenders are disproportionately comprised of individuals born with
neurological deficits and reared by caregivers without the skills and resources to effectively socialize a difficult
child.
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关键字 Classification error, Latent class analysis, Mixture models.
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