Biography
Soumik's research is mainly focused on developing multistage sampling procedures to estimate parameters under a given model. It is a combination of including both point and interval estimation scenarios. It proves to be useful whenever a fixed sample technique fails to deliver the desired results. The broad idea is to collect samples in stages or one at a time as required by a stopping rule. Assuming that one needs to estimate a population parameter under a specific distribution and under a pre-defined loss function. Further, assume that this estimation must be carried out while considering a “minimized risk” situation. It turns out that there are no fixed sample solutions to this problem, and one must switch to a specific multistage sampling route.
These methods have been applied widely over a range of areas such as health, ecology, finance, quality control, etc. In addition, there has been significant research both under frequentist and Bayesian frameworks.