'Correlation structure' diaporamas de présentation

Lecture 4 (Chapter 4)

Lecture 4 (Chapter 4). Linear Models for Correlated Data. We aim to develop a general linear model framework for longitudinal data, in which the inference we make about the parameters of interest recognize the likely correlation structure of the data. There are two ways of achieving this:

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New Trends in Energy Derivatives

New Trends in Energy Derivatives. Alexander Eydeland Morgan Stanley. Increased interest in commodity-linked products: the investors point of view. spectacular returns in the last few years diversification

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Using the Maryland Biological Stream Survey Data to Test Spatial Statistical Models

Using the Maryland Biological Stream Survey Data to Test Spatial Statistical Models. A Collaborative Approach to Analyzing Stream Network Data. Andrew A. Merton. Overview. The material presented here is a subset of the work done by Erin Peterson for her Ph.D.

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HSRP 734: Advanced Statistical Methods June 19, 2008

HSRP 734: Advanced Statistical Methods June 19, 2008. Extensions of Logistic Regression. Outcomes with more than 2 categories Categories have order Unordered Conditional logistic regression Analysis of matched data. Extensions of Logistic Regression. Exact methods for small samples

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Discrete Choice Modeling

William Greene Stern School of Business New York University. Discrete Choice Modeling. Part 5.2. The Nested Logit Model. Extended Formulation of the MNL. Clusters of similar alternatives Compound Utility: U(Alt)=U(Alt|Branch)+U(branch)

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Discrete Choice Modeling

William Greene Stern School of Business New York University. Discrete Choice Modeling. 0 Introduction 1 Methodology 2 Binary Choice 3 Panel Data 4 Bivariate Probit 5 Ordered Choice 6 Count Data 7 Multinomial Choice 8 Nested Logit 9 Heterogeneity 10 Latent Class 11 Mixed Logit

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Repeated Measures, Part 3

Repeated Measures, Part 3. Charles E. McCulloch, Division of Biostatistics, Dept of Epidemiology and Biostatistics, UCSF. May, 2012. Outline. More on XTGEE Examples Robust standard errors Binary outcomes Changing the link function Modeling practice Summary. More on xtgee.

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Repeated Measures, Part 3

Repeated Measures, Part 3. Charles E. McCulloch, Division of Biostatistics, Dept of Epidemiology and Biostatistics, UCSF. May, 2009. Outline. More on XTGEE Examples Robust standard errors Binary outcomes Changing the link function Modeling practice Summary. More on xtgee.

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Stochastic Mortality, Macroeconomic Risks, and Life Insurer Solvency

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Factor Analysis

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Symmetric Disparity Estimation in Distributed Coding of Stereo Images

Symmetric Disparity Estimation in Distributed Coding of Stereo Images. Xin Li Lane Dept. of CSEE West Virginia University Morgantown, WV 26506-6109. Outline. Background: distributed source coding (DSC)

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Implementation of Quasi-Least Squares using xtgee in Stata

Implementation of Quasi-Least Squares using xtgee in Stata. Justine Shults Assistant Professor of Biostatistics Department of Biostatistics University of Pennsylvania School of Medicine. Outline. Motivational Study -Traditional Approach (GEE) Describe quasi-least squares (QLS)

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On Anomalous Hot Spot Discovery in Graph Streams

On Anomalous Hot Spot Discovery in Graph Streams. 2013-12-08 @Dallas. Introduction. Background We care about data stream of interactions between network participants. Social Network, Communication Network, etc.

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STA 216 Generalized Linear Models

STA 216 Generalized Linear Models. Meets : 2:50-4:05 T/TH (Old Chem 025) Instructor : David Dunson 219A Old Chemistry, 684-8025 dunson@stat.duke.edu Teaching Assistant : Dawn Barnard 112 Old Chemistry, 684-4365 dawn@stat.duke.edu. STA 216 Syllabus. Topics to be covered:

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Application

EbT. CtS. Distributed Source Coding of Correlated Data from Image Sensors. Conclusions and Future Work. X 1. X 2. BSC. p. Non-uniform Channels. X 1. Correlation. X 2. Channel. Rn. Systematic. ( X 2 ,P X 2 ). X 2. X 2. Channel. Decoder. n.

74 views • 1 slides