PPT-9. Heterogeneity: Latent Class Models

Latent Classes A population contains a mixture of individuals of different types classes Common form of the data generating mechanism within the classes Observed outcome y is governed by the common process . Hongning Wang, . Yue. Lu, . ChengXiang. . Zhai. {. wang296,yuelu2,czhai. }@cs.uiuc.edu. Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL, 61801 USA. 1. Kindle 3. iPad. Department of Economics. Stern School of Business. New York University. Latent Class Modeling. Outline. Finite mixture and latent class models . Extensions of the latent class model. Applications of several variations. William Greene. Stern School of Business. New York University. 0 Introduction. 1 . Summary. 2 Binary Choice. 3 Panel Data. 4 Bivariate Probit. 5 Ordered Choice. 6 Count Data. 7 Multinomial Choice. 8 Nested Logit. David K. . Guilkey. Demographic Applications:. Single Spell. 1. Time until death. 2. Time until retirement. 3. Time until first marriage. 4. Time until first birth. Multiple Spell. 1. Time until birth of each child. Latent Classes. A population contains a mixture of individuals of different types (classes). Common form of the data generating mechanism within the classes. Observed outcome y is governed by the . common process . Harvey Goldstein. Centre for Multilevel Modelling. University of Bristol. The (multilevel) binary . probit. model. . Suppose . that we have a variance components 2-level model for . an . underlying continuous variable written as . C. ontents:. Phases of matter. Changing phase. Latent heat. Graphs of phase change. Whiteboard. Graph whiteboards. 4 Phases of Matter. TOC. Solid. Crystalline/non crystalline. Liquid. Greased marbles. Supervisor: Dr. Doug King. Niloofar. . Alavi. Background: Biodiversity and Habitat . H. eterogeneity. Biodiversity:. . T. he . variability among living organisms from all sources including, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part . Part II: Definition and Properties. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . Alexander Kotov. 1. , . Mehedi. Hasan. 1. , . April . Carcone. 1. , Ming Dong. 1. , Sylvie Naar-King. 1. , Kathryn Brogan Hartlieb. 2. . 1 . Wayne State University. 2 . Florida International University. Emma Mead. Methodologist at the . Cochrane Skin . Group, University of Nottingham. Research associate and PhD student, Teesside University. Email: . Emma.Mead@nottingham.ac.uk. Dr . Ben . Carter. Statistics Editor for the Cochrane Skin Group, . October 28, 2016. Objectives. For you to leave here knowing…. What is the LCR model and its underlying assumptions?. How are LCR parameters interpreted?. How does one check the assumptions of an LCR model?. Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation. A Gentle Introduction…. Hopefully. Angela B. Bradford, PhD, LMFT. School of Family Life. Brigham Young University. Background. Mixture Models (aka “finite mixture models”)- Models based on the idea that there are multiple characteristically different sub-populations within the population, and that those subpopulations are not directly observable. Mixture models characterize and estimate parameters for those sub-populations.