JSM Activity #CE_07CThis is the preliminary program for the 2004 Joint Statistical Meetings in Toronto, Canada. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2004); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions. To View the Program: You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time. |
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Legend: = Applied Session,
= Theme Session,
= Presenter FRY = Fairmont Royal York, ICH = InterContinental Hotel, TCC = Metro Toronto Convention Center |
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CE_07C | Sun, 8/8/04, 8:00 AM - 4:00 PM | FRY-Tudor 8 |
Regression Modeling Strategies (1 Day Course) - Continuing Education - Course | ||
ASA | ||
Instructor(s): Frank E. Harrell, Jr., Vanderbilt University School of Medicine | ||
All standard regression models have assumptions that must be verified for the model to have power to test hypotheses and for it to be able to predict accurately. Of the principal assumptions (linearity, additivity, distributional), this course will emphasize methods for assessing and satisfying the first two. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response using the widely applicable method of augmenting the design matrix using restricted cubic splines. Even when assumptions are satisfied, overfitting can ruin a model's predictive ability for future observations. Methods for data reduction will be introduced to deal with the common case where the number of potential predictors is large in comparison with the number of observations. Methods of model validation (bootstrap and cross-validation) will be covered, as will auxiliary topics such as modeling interaction surfaces, efficiently utilizing partial covariable data by using multiple imputation, variable selection, overly influential observations, collinearity, and shrinkage. The methods covered will apply to almost any regression model, including ordinary least squares, logistic regression models, and survival models. | ||
JSM 2004
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