PPT-Right-Sizing Growth Mixture Models for Longitudinal Data

Phillip Wood Wolfgang Wiedermann Douglas Steinley University of Missouri Some Questions We Wish We Could Answer with Longitudinal Data Are there Different Types of Learners Slow Versus Quick. Proposed JCP Performance Prediction Models for PMIS. 1. Presentation Outline. Overview of JCP distresses in PMIS. Original models and recalibration objectives. Methodology. Data treatment. Estimated age. Alan Ritter. Latent Variable Models. Previously: learning parameters with fully observed data. Alternate approach: hidden (latent) variables. Latent Cause. Q: how do we learn parameters?. Unsupervised Learning. Mixture Models and Expectation Maximization. Machine Learning. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. Gaussian Mixture Models. Expectation Maximization. The Problem. 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 . Select a Material Model to Launch. Pure Gas Models. Gas Models. Gas Mixture Models. Binary Mixture. General Mixture. RG Model. RG+RG Model. PG Model. PG+PG Model. IG+IG Model. n-IGE Model. n-IG Model. Select a Material Model to Launch. Pure Gas Models. Gas Models. Gas Mixture Models. Binary Mixture. General Mixture. IG Model. RG Model. RG+RG Model. PG Model. PG+PG Model. IG+IG Model. n-IGE Model. Machine Learning. April 13, 2010. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. A brief look at Homework 2. Gaussian Mixture Models. Expectation Maximization. The Problem. 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. OBJECTIVE. Find functions that satisfy . dP. /. dt. = . kP. .. Convert between growth rate and doubling time.. Solve application problems using exponential growth and limited growth models.. 3.3 Applications: Uninhibited and Limited Growth Models. Mikhail . Belkin. Dept. of Computer Science and Engineering, . Dept. of Statistics . Ohio State . University / ISTA. Joint work with . Kaushik. . Sinha. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . McLachlan, G., & Peel, D. (2001). . Finite mixture models. . New York: Wiley.. Murphy, K. P. (2013). . Machine learning: a probabilistic perspective. . Cambridge, Mass.: MIT Press.. Bishop, C. M. (2013). . 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. Chuck Huber, PhD. StataCorp. chuber@stata.com. Yale University. November 2, 2018. Outline. Introduction to Multilevel Models. Introduction to Longitudinal Models. Introduction to Bayesian Analysis. Bayesian . an . introduction to . using cohort data. Dr. David Bann . co-Investigator . of . NCDS. Dr. Morag Henderson . co-Investigator of . Next Steps. Dr. . Vanessa . Moulton . Research Associate. Tremendous interest in cohort studies – powerful resources .