Associate Professor, National Institute of Technology, Rourkela
Project Overview
Researchers are increasingly interested in classifying observations into multiple groups or populations under various probabilistic model assumptions. This problem has applications in fields like medicine, agriculture, social sciences, military surveillance, image processing, and pattern recognition. The project aims to develop new classification rules using censored training data, using probabilistic models like gamma, Weibull, log-normal, and Bur XII. Simulation methodology will be used to derive new classification rules and estimate the expected probability of misclassification.