PPT-Multiple Imputation in Finite Mixture Modeling

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Daniel Lee Presentation for MMM conference May 24 2016 University of Connecticut 1 2 Introduction Finite Mixture Models Class of statistical models that treat group

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Multiple Imputation in Finite Mixture Modeling: Transcript

Daniel Lee Presentation for MMM conference May 24 2016 University of Connecticut 1 2 Introduction Finite Mixture Models Class of statistical models that treat group membership as a latent categorical variable. Estie Hudes. Tor . Neilands. UCSF . Center for AIDS Prevention . Studies. Part 2. January 18, 2013. 1. Contents. 1. Summary of Part 1. 2. EM Algorithm . 3. Multiple Imputation (MI) for normal data. 4. Multiple Imputation (MI) for mixed data. Trivellore Raghunathan. Chair and Professor of Biostatistics, School of Public Health. Research Professor, Institute for Social Research. University of Michigan. Presented at the National Conference on Health Statistics, August 16-18, 2010 . longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . María. . García. , Chandra Erdman, and Ben Klemens. Outline. Background on the Survey of Income and Program Participation (SIPP). Methods for missing data imputation. - . Randomized Hot deck. - SRMI . Robert M. Baskin, Samuel H. Zuvekas and Trena M. Ezzati-Rice. Division of Statistical Methods and Research. Center for Financing, Access and Cost Trends. Purpose of Study. Use Fraction of Missing Information (FMI) to evaluate new item imputation . with large proportions of missing data. :how much is too much? . Texas A&M HSC . Jin. is designed by . Dr. Huber. Korean Female Colon Cancer. Risk. Factors. Range. Event . Non-event. HR. 95% CI. Katherine Lee. Murdoch Children’s Research Institute &. University of Melbourne. Missing data in epidemiology & clinical research. Widespread problem, especially in long-term follow-up studies. Cattram Nguyen, Katherine Lee, John . Carlin. Biometrics by the Harbour, 30 Nov, 2015. Motivating example: Longitudinal Study of Australian Children (LSAC). 5107 infants (0-1 year) recruited in 2004. f. or sensitivity analysis of clinical trials with missing data. Suzie Cro. MRC Clinical Trials Unit at UCL. The London School of Hygiene and Tropical Medicine. Outline. Reference based multiple imputation; asthma trial. longitudinal health . records. Irene Petersen and Cathy Welch. Primary Care & Population Health. Today. Issues with missing data and multiple imputation of longitudinal records. Twofold algorithm . Al M Best, PhD. Virginia Commonwealth University. Task Force on Design and Analysis . in Oral Health Research. Satellite Symposium, AADR. Boston, MA: March 10, 2015. Multivariable statistical modeling from 10,000 feet. Trang Quynh Nguyen, May 9, 2016. 410.686.01 Advanced Quantitative Methods in the Social and Behavioral Sciences: A Practical Introduction. Objectives. Provide a QUICK introduction to latent class models and finite mixture modeling, with examples. Tor . Neilands. UCSF . Center for AIDS Prevention . Studies. Part 2. January 18, 2013. 1. Contents. 1. Summary of Part 1. 2. EM Algorithm . 3. Multiple Imputation (MI) for normal data. 4. Multiple Imputation (MI) for mixed data. – . 2. Introduction. Many linear inverse problems are solved using a Bayesian approach assuming Gaussian distribution of the model.. We show the analytical solution of the Bayesian linear inverse problem in the Gaussian mixture case..

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