Presenter:
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Linda J. Young, University of Florida |
Date:
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One-day course, April 21, 2011 |
Location:
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Cecil College
Room 208 of Building D (the Conference Center)
North East, Maryland
(www.cecil.edu/about/map/northeast.asp)
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Registration Fee:
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$60 (including light breakfast and lunch)
Registration is limited to 60 participants.
Registration deadline is April 13 if course isn't full before that date.
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| Registration is Closed. |
| Questions about the course can be addressed to Stephen F. Bingham at stephen.bingham@va.gov or 410-642-2411 ext 5301. |
Abstract |
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| Data sets from designed experiments, sample surveys, and observational studies often contain correlated observations due to random effects and repeated measures. Mixed models can be used to accommodate the correlation structure, produce efficient estimates of means and differences between means, and provide valid estimates of standard errors. Repeated measures and longitudinal data require special attention because they involve correlated data that arise when the primary sampling units are measured repeatedly over time or under different conditions. Normal theory models for random effects and repeated measures ANOVA will b used to introduce the concept of correlated data. These models are then extended to generalized linear mixed models for the analysis of non-normal data, including binomial responses, Poisson counts, and over-dispersed count data. Methods of assessing the fit and deciding among competing models will be discussed. Accounting for spatial correlation and radial smoothing splines within mixed models will be presented and their application illustrated. The use of SAS System's PROC GLIMMIX will be introduced as an extension of PROC MIXED and used to analyze data from pharmaceutical trials, environmental studies, educational research, and laboratory experiments. |
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Who Would Benefit? |
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| This short course is for those who want to learn about the theory and application of linear and generalized linear mixed models. The material is presented at an applied level, accessible to participants with training in linear statistical models and previous exposure to linear mixed models. Some experience with SAS's PROC MIXED would be helpful. |
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Biographical Sketch |
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| Dr. Linda J. Young is a Professor of Statistics at the University of Florida where she teaches, consults, and conducts research on statistical methods for studies in public health, agricultural, environmental, and ecological settings. Dr. Young has a Ph.D. from Oklahoma State University. She has been a faculty member at Oklahoma State University, the University of Nebraska, and the University of Florida. Dr. Young has more than 100 publications in over 50 different journals, constituting a mixture of statistics and subject-matter journals, and two books with a third one currently in press. A major component of her work is collaborative with researchers in the agricultural, ecological, environmental, and health sciences. Her recent research has focused on linking disparate data sets and the subsequent analysis of these data using spatial statistical methods. Dr. Young has been the editor of the Journal of Agricultural, Biological and Environmental Statistics. She is currently associate editor for Biometrics, Journal of Environmental and Ecological Statistics, and Sequential Analysis. Dr. Young also has a keen interest in statistics education at all levels, having worked with students and teachers from Kindergarten through High School as well as undergraduate, graduate, and post-graduate training. Dr. Young has served in a broad range of offices within the professional statistical societies, including President of the Eastern North American Region of the American Statistical Association, Vice-President of the American Statistical Association, Chair of the Committee of Presidents of Statistical Societies, and member of the National Institute of Statistical Science's Board of Directors. Dr. Young is a recipient of the American Statistical Association's Founders Award, a fellow of the American Statistical Association and an elected member of the International Statistical Institute. She has served on numerous panels for the National Science Foundation and the Environmental Protection Agency.
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Schedule |
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| 8:00-8:30 |
Light Breakfast
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| 8:30-10:10 |
Fixed and Random Effects
Split plot analysis
Linear Mixed Models and Generalized Linear Mixed Models
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| 10:30-12:00 |
Modeling the Covariance Structure in a Repeated Measures Setting |
| 12:00-1:00 |
Lunch |
| 1:00-2:30 |
Accounting for Spatial Variability |
| 2:50-4:00 |
More Advanced Correlation Structures |
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