'Log likelihood' diaporamas de présentation

Discrete Choice Modeling

William Greene Stern School of Business New York University. Discrete Choice Modeling. Econometric Methodology. Objectives in Model Building. Specification : guided by underlying theory Modeling framework Functional forms Estimation : coefficients, partial effects, model implications

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Temporomandibular disorders (TMD) Occlusion and Orthodontic treatment

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8. Heterogeneity: Latent Class Models

8. 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 F( y| x , ? j )

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Model Selection/Comparison

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Executive Popularity in France: The Promise and Pitfalls of Time Series Data

Executive Popularity in France: The Promise and Pitfalls of Time Series Data . Research and Methods Symposium, October 1 st 2004. The problems. The substantive problem: how do macroeconomic conditions affect support for the dual executive (president and prime minister) in France?

656 views • 50 slides

Tackling meaning and aboutness with KeyWords

Tackling meaning and aboutness with KeyWords. Mike Scott, School of English University of Liverpool. Corpus Linguistics Summer Institute Liverpool 2 July 2008. Purpose. To explore the notion of keyness and its implications in corpus-based study with reference to WordSmith. Keyness.

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William Greene Stern School of Business New York University

William Greene Stern School of Business New York University. 1 Introduction 2 Binary Choice 3 Panel Data 4 Ordered Choice 5 Multinomial Choice 6 Heterogeneity 7 Latent Class 8 Mixed Logit 9 Stated Preference. Application: Health Care Panel Data.

589 views • 46 slides

Multilevel Linear Modeling

Multilevel Linear Modeling. aka HLM. The Design. We have data at two different levels In this case, 7,185 students (Level 1) Nested within 160 Schools (Level 2) We want to investigate effects at both levels. Get the Data. Download data file in XLS format Print the Cheat-Sheet

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Logistic Regression

STAT E-150 Statistical Methods. Logistic Regression. So far we have considered regression analyses where the response variables are quantitative . What if this is not the case? If a response variable is categorical a different regression model applies, called logistic regression .

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Multinomial Logistic Regression: A Problem in Personnel Classification

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Joint Enhancement of Topic Modeling and Information Network Mining

Joint Enhancement of Topic Modeling and Information Network Mining. Mid-Year PI Report focusing on I3.2 Heng Ji City University of New York NSCTA/INARC. INARC Project Major Contributions. I3.2-Subtask 1:

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Natural Language Processing (Almost) from Scratch

Natural Language Processing (Almost) from Scratch Ronan Collobert et al., J. of Machine Learning Research, 2011. Guanqun Yang, Pencheng Xu, Haiqi Xiao, Yuqing Wang. Overview. Introduction Benchmark t asks Neural network architecture Techniques to improve the performance of NN

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More on Parametric and Nonparametric Population Modeling: a brief Summary

More on Parametric and Nonparametric Population Modeling: a brief Summary. Roger Jelliffe, M.D. USC Lab of Applied Pharmacokinetics

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Hidden Markov Models

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Determining the Neutrino Hierarchy From a Galactic Supernova

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Robust Object Tracking with Online Multiple Instance Learning

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Advantages of using MSM.

Advantages of using MSM. Can using existing software (such as Proc Genmod in SAS, glm in Splus and R, logit in STATA). Easily get tests and confidence intervals. Returns parameter estimates that have more direct public health interpretations than, for example, the traditional Odds Ratio (OR).

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LECTURE 06: MAXIMUM LIKELIHOOD ESTIMATION

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LECTURE 05: MAXIMUM LIKELIHOOD ESTIMATION

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