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Adaptations of GLM for Advanced Data Analysis
Sat, April 14, 4:05 to 5:35pm, Millennium Broadway New York Times Square, Floor: Third Floor, Room 3.04-3.05Session Type: Paper Session
Abstract
This program focuses on specialized adjustments to GLM that has to be considered during data analysis. These issues include missing data, autocorrelations in longitudinal analyses, low incidence in logistic regression, multicollinearity, canonical correlations.
Sub Unit
Chair
Papers
Addressing Autocorrelation in Time Series - Shenira A. Perez, Boston College; Larry H. Ludlow, Boston College
Commonality Analysis of Multivariate Measures of Association - Kim Nimon, The University of Texas - Tyler; Linda Reichwein Zientek, Sam Houston State University
Logistic Regression Under Sparse Data Conditions - Thomas J. Smith, Northern Illinois University; David A. Walker, Northern Illinois University; Cornelius McKenna, Kishwaukee College
Multicollinearity's Effect on Real Data Structures - Mary G. Lieberman, Florida Atlantic University; John D. Morris, Florida Atlantic University
Multiple Imputation for Missing Data Analysis in Proportional Odds Models for Ordinal Response Variables - Xing Liu, Eastern Connecticut State University; Haiyan Bai, University of Central Florida; Hari P. Koirala, Eastern Connecticut State University