David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error, and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Electronic Journal of Statistics , former Editor of the Institute of Mathematical Statistics' Lecture Notes Monographs Series , and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and several books.
Introduction. Important Concepts. Linear Regression and Attenuation.
Regression Calibration. Simulation Extrapolation. Instrumental Variables.
Score Function Methods. Likelihood and Quasilikelihood. Bayesian Methods.
Hypothesis Testing. Longitudinal Data and Mixed Models. Nonparametric
Estimation. Semiparametric Regression. Survival Data. Response Variable
Error. Appendix A: Background Material. Appendix B: Technical Details.
References. Applications and Examples Index. Index.
