During his undergraduate studies, H. Karamikabir undertook a range of courses covering foundational and advanced statistical topics. These included Probability, Mathematical Statistics, Introduction to Statistics and Probability, Statistical Methods, Regression, Multivariate Statistical Method, Official Statistics, Accounting Statistics, Engineering Statistics, Computational Statistics, and Biostatistics. In his graduate and postgraduate coursework, H. Karamikabir delved into more specialized and advanced topics. He completed courses such as Advanced Computational Statistics and Simulation, Statistical Analysis for Accounting, Management, Biology, and Geography, and Statistical Learning. These courses provided him with a deeper understanding of statistical methodologies and their application across various disciplines.
Research Interest:
Dr. Hamid Karamikabir’s fields of research and interests encompass a wide array of statistical areas, reflecting his expertise and passion for the field. His focus includes Multivariate Statistical Inference, where he explores complex relationships and patterns within multivariate datasets. Statistical Learning and Machine Learning are also core areas of interest, highlighting his keenness to leverage advanced computational techniques for data analysis and prediction. High Dimensional Data is another area of his research, indicating his work with datasets that have a large number of variables, requiring specialized techniques for analysis. Dr. Karamikabir also specializes in Shrinkage Estimators, which are methods used to improve the estimation of parameters by shrinking the effect of irrelevant variables or noise.