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iterative expectation-maximization (em) algorithm  (SAS institute)


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    SAS institute iterative expectation-maximization (em) algorithm
    Iterative Expectation Maximization (Em) Algorithm, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/expectation-maximization+(em)+algorithm/expectation+maximization++em++algorithm/pmc10071823-197-43-35
    Average 90 stars, based on 1 article reviews
    iterative expectation-maximization (em) algorithm - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Do young people benefit from AA as much, and in the same ways, as adult aged 30+? A moderated multiple mediation analysis.
    Article Snippet: Background: Research has shown that participation in Alcoholics Anonymous (AA) confers significant recovery benefit to adults suffering from alcohol use disorder (AUD).. Concerns persist, however, that AA may not work as well for younger adults, who tend to have shorter addiction histories, different social circumstances, and less spiritual/religious interest than adults.. Methods: Secondary data analysis of Project MATCH, using a prospective, moderated multiple mediation analysis to test and compare six previously identified mechanisms of change in younger adults (n = 266) vs. adults aged 30+ (n = 1460).

    Article Title: A Peer-Led High School Transition Program Increases Graduation Rates Among Latino Males
    Article Snippet: Missing data at baseline were imputed using the Expectation-Maximization (EM) algorithm approach to imputation in SAS version 9.1 under the assumption that missing data occurred at random.

    Article Title: Individual and Situational Factors that Influence the Efficacy of Personalized Feedback Substance Use Interventions for Mandated College Students
    Article Snippet: We used the expectation maximization (EM) algorithm for maximum likelihood (ML) estimation for missing data imputation using SAS ( SAS Institute, 2002-2006 ), after the Little's chi-square test of Missing Completely At Random (MCAR test; Little, 1988 ) resulted in a non-significant chi-square of 8078.96 ( df = 8020), p > .05, indicating that missing values were a random subset of the complete data.

    Article Title: Sex-specific association of Epidermal Growth Factor gene polymorphisms with ARDS
    Article Snippet: Haplotype frequencies were estimated from the unphased genotype data in the combined population (cases and controls), using the expectation maximization (EM) algorithm as implemented in SAS/Genetics.

    Article Title: Beyond neighborhood poverty: family management, neighborhood disorder, and adolescents' early sexual onset.
    Article Snippet: The authors examined how neighborhood disorder modifies associations between family management practices and youth transitions to sex among low-income African American and Latino urban families.. The sample included 846 young adolescents and their mothers who participated in Welfare, Children and Families: A Three-City Study.. Results from multilevel logistic regression models indicated no main effects of family management practices or neighborhood-level conditions on transitions to sex once accounting for demographics.

    Article Title: Disparate Associations of HLA Class I Markers with HIV-1 Acquisition and Control of Viremia in an African Population
    Article Snippet: HLA haplotypes and supertypes were assigned using the expectation-maximization (EM) algorithm in SAS Genetics (SAS Institute, Cary, NC) , .

    Article Title: Development of and Field-Test Results for the CAHPS PCMH Survey
    Article Snippet: We imputed missing data in factor analyses using SAS PROC MI (SAS Version 9.2) and obtained maximum likelihood estimates of the covariance matrix using expectation-maximization (EM) algorithm (SAS PROC MI, SAS version 9.2).

    Article Title: Individual and Situational Factors that Influence the Efficacy of Personalized Feedback Substance Use Interventions for Mandated College Students
    Article Snippet: 6 Missing Data We used the expectation maximization (EM) algorithm for maximum likelihood (ML) estimation for missing data imputation using SAS ( SAS Institute, 2002-2006 ), after the Little's chi-square test of Missing Completely At Random (MCAR test; Little, 1988 ) resulted in a non-significant chi-square of 8078.96 ( df = 8020), p > .05, indicating that missing values were a random subset of the complete data.



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