A maximum likelihood approach to test validation with missing and censored dependent variables

Autor:	Alan L. Gross
Rok vydání:	1990
Předmět:	Restricted maximum likelihood Estimation theory Applied Mathematics Expectation–maximization algorithm Statistics Latent variable Imputation (statistics) Maximum likelihood sequence estimation Missing data Censoring (statistics) General Psychology Mathematics
Zdroj:	Psychometrika. 55:533-549
ISSN:	1860-0980 0033-3123
Popis:	A maximum likelihood approach is described for estimating the validity of a test (x) as a predictor of a criterion variable (y) when there are both missing and censoredy scores present in the data set. The missing data are due to selection on a latent variable (y s ) which may be conditionally related toy givenx. Thus, the missing data may not be missing random. The censoring process in due to the presence of a floor or ceiling effect. The maximum likelihood estimates are constructed using the EM algorithm. The entire analysis is demonstrated in terms of hypothetical data sets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7de0ad8d919f5988da9fc5ff099095bd https://doi.org/10.1007/bf02294766 Zobrazit plný text záznamu Full text from SpringerLink