This book contains select chapters on a range of topics in directional statistics, multivariate statistical inference, financial statistics, statistical machine learning and reliability inference. At the 43rd Annual Convention of the Indian Society for Probability and Statistics (ISPS) held in Prayagraj (formerly Allahabad), Uttar Pradesh, India, from 6–8 February 2024, attribute was paid to Prof. Ashis SenGupta on the occasion of his 70th birthday. He has pioneered research on directional statistics in the modern era in India and enhanced it worldwide and contributed significantly to the advancement of the following topics:Highly flexible distributions on manifoldsStatistical machine learning in data scienceBig data on manifoldsOptimal multiparameter, multivariate statistical inferenceReliability inference and stress-dependent-strength modelsDirectional statistics for highly volatile financial modelsCylindrical, spherical and toroidal regression analysisInnovative applications of emerging real-life directional data

This book contains select chapters on a range of topics in directional statistics, multivariate statistical inference, financial statistics, statistical machine learning and reliability inference. At the 43rd Annual Convention of the Indian Society for Probability and Statistics (ISPS) held in Prayagraj (formerly Allahabad), Uttar Pradesh, India, from 6–8 February 2024, attribute was paid to Prof. Ashis SenGupta on the occasion of his 70th birthday. He has pioneered research on directional statistics in the modern era in India and enhanced it worldwide and contributed significantly to the advancement of the following topics:Highly flexible distributions on manifoldsStatistical machine learning in data scienceBig data on manifoldsOptimal multiparameter, multivariate statistical inferenceReliability inference and stress-dependent-strength modelsDirectional statistics for highly volatile financial modelsCylindrical, spherical and toroidal regression analysisInnovative applications of emerging real-life directional data