Handbook for Applied Modeling: Non-Gaussian and Correlated Data - Riggs, Jamie D

Résumé :
This compact, entry-level Handbook equips applied practitioners to choose and use core models for real-world data - with R and SAS.

Biographie:
Jamie D. Riggs is an adjunct lecturer in the Predictive Analytics program at Northwestern University, Illinois. She specializes in the statistical issues of solar system cratering processes, solar physics, and galactic dynamics, and has collaborated with researchers at the Los Alamos National Laboratory, New Mexico and the Southwest Research Institute, Texas. She has held technical and managerial positions at Sun Microsystems, Inc., National Oceanic and Atmospheric Administration, and the Boeing Company, where she applied advanced statistical designs and analyses to manufacturing and business problems. She is the Solar System and Planetary Sciences Section Head of the International Astrostatistics Association.

Sommaire:
1. The data sets; 2. The model-building process; 3. Constance variance response models; 4. Non-constant variance response models; 5. Discrete, categorical response models; 6. Counts response models; 7. Time-to-event response models; 8. Longitudinal response models; 9. Structural equation modeling; 10. Matching data to models.