Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient...
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| Veröffentlicht in: | Biometrics 1999-06, Vol.55 (2), p.463-469 |
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| creator | Muthén, Bengt Shedden, Kerby |
| description | This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed. |
| doi_str_mv | 10.1111/j.0006-341X.1999.00463.x |
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As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.</description><subject>Adolescent</subject><subject>Adult</subject><subject>alcohol abuse</subject><subject>Alcohol drinking</subject><subject>Alcohol Drinking - adverse effects</subject><subject>alcoholic beverages</subject><subject>Alcoholism - etiology</subject><subject>Alcohols</subject><subject>Algorithms</subject><subject>Analytical estimating</subject><subject>Biometry</subject><subject>Covariance matrices</subject><subject>Female</subject><subject>Growth modeling</subject><subject>growth models</subject><subject>Humans</subject><subject>Latent class analysis</subject><subject>Latent variables</subject><subject>Likelihood Functions</subject><subject>Logistic regression</subject><subject>Male</subject><subject>Maximum likelihood</subject><subject>Modeling</subject><subject>Models, Statistical</subject><subject>Normativity</subject><subject>Parametric models</subject><subject>prediction</subject><subject>probability</subject><subject>Standard error</subject><subject>Trajectories</subject><subject>Trajectory classes</subject><subject>young adults</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkE9PGzEQxS3UCgLtN0Cw6n2D7Vl74wMHyn-JNKpogNvIm7WDt0kW7F0Rvj1eNkqv9WU8fr83Hj1CEkaHLJ6TakgplSlk7GnIlFKxzSQM1ztkwETGUppx-oUMttAe2Q-hiq0SlO-SPcaAjThlA3Jx5VauMcnYrZvWx1qXZuFW8-TNNc_b10nbzOqlCck0dFrzbJLLcXK2mNc-Ystv5KvVi2C-b-oBmV5d_jm_Se8m17fnZ3fpTACFVBSaiwIYBU2z0paF4tIWBRWZ4SBBAWWZpTlwHUGrNJQzo42ynCottWVwQH70c198_dqa0GBVt34Vv0TOmRwpSXmERj0083UI3lh88W6p_Tsyil16WGEXDHbBYJcefqaH62g92sxvi6Up_xk3cUXgtAfe3MK8__dg_Hk7Gcdb9B_2_io0td_6uQDIVSenvexCY9ZbWfu_KHPIBT7-ukYpbtjD76d7zCN_3PNW16jn3gWc3sc9RbeGVDKDD4sKnqs</recordid><startdate>199906</startdate><enddate>199906</enddate><creator>Muthén, Bengt</creator><creator>Shedden, Kerby</creator><general>Blackwell Publishing Ltd</general><general>International Biometric Society</general><scope>FBQ</scope><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88I</scope><scope>8AF</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>199906</creationdate><title>Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm</title><author>Muthén, Bengt ; Shedden, Kerby</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5303-5ba25b3103a04dfdb926fbb054e236393014f0732aba2f9a3dceae9f209a6af13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>alcohol abuse</topic><topic>Alcohol drinking</topic><topic>Alcohol Drinking - 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The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>11318201</pmid><doi>10.1111/j.0006-341X.1999.00463.x</doi><tpages>7</tpages></addata></record> |
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| source | Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); MEDLINE; Wiley Online Library Journals Frontfile Complete; JSTOR Mathematics & Statistics |
| subjects | Adolescent Adult alcohol abuse Alcohol drinking Alcohol Drinking - adverse effects alcoholic beverages Alcoholism - etiology Alcohols Algorithms Analytical estimating Biometry Covariance matrices Female Growth modeling growth models Humans Latent class analysis Latent variables Likelihood Functions Logistic regression Male Maximum likelihood Modeling Models, Statistical Normativity Parametric models prediction probability Standard error Trajectories Trajectory classes young adults |
| title | Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm |
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