Multivariate Analysis, Design of Experiments, and Survey Sampling-Contributions of Jagdish N. Srivastava in Statistical Planning and Inference
Subir Ghosh
Department of Statistics, University of California, Riverside, United States

Planning and Inference are the two twin branches of statistics, the former being concerned with how to collect data (or information) and the latter with how to analyze or summarize the information after it has been collected. The data may be collected in one or more attempts, one or more variables, and may be subject to relatively simple or complex stochastic processes. In this world of statistics, Jagdish N. Srivastava will be remembered for his leadership in statistics profession; his thought provoking, penetrating, and deeper questions at the professional meetings and conferences; and his pioneering research contributions in design of experiments, as well as in multivariate analysis, survey sampling, reliability, coding theory, combinatorial theory, and many other areas of statistics and mathematics. This presentation first highlights major contributions of Jagdish N. Srivastava and then goes into the model search, identification, discrimination for finding the best model from a class of models possibly could describe the data to be collected in an experiment designed to answer some scientific questions.

Keywords: Planning; Inference; Design; Model Search

Biography: Subir Ghosh is Professor of Statistics at University of California, Riverside, USA