Statistical Modeling and Applications on Real-Time Problems

Autorentext

Chandra Shekhar is a distinguished academician and the former Head of the Department of Mathematics at BITS Pilani, India. He is actively engaged in both research and teaching, and his expertise encompasses a wide range of mathematical fields. These areas include Queueing Theory, Computer and Communication Systems, Machine Repair Problems, Reliability and Maintainability, Stochastic Processes, Evolutionary Computation, Statistical Analysis, and Fuzzy Set & Logic. At both the undergraduate and postgraduate levels, he imparts knowledge in subjects such as Probability and Statistics, Differential Equations, Linear Algebra, Advanced Calculus, Complex Variables, Fuzzy Logic, Operations Research, Statistical Inference, and more. He is a pioneer in Evolutionary Computation, Markovian and Stochastic Modeling, Queuing Analysis and its Applications, Inventory Theory, and Reliability Theory. He actively participates in national and international conferences, Faculty Development Programs (FDPs), and has taken the lead in organizing numerous conferences, workshops, and symposiums as a convener and organizing secretary. He has received the Best Research Paper Award at an international conference. His contributions extend to scholarly publications, with over 50 research articles published in esteemed journals within these domains. Additionally, he has supervised three Ph.D. theses and authored book chapters in edited books published by globally recognized publishers. Chandra Shekhar's authorship includes textbooks such as "Differential Equations, Calculus of Variations, and Special Functions," as well as edited books titled "Mathematical Modeling and Computation of Real-Time Problems: An Interdisciplinary Approach" and "Modeling and Applications in Operations Research." Beyond his academic roles, Chandra Shekhar actively serves as a member of editorial boards and as a reviewer for prestigious journals and academic societies. He also contributes his expertise to various academic committees, including Hon'ble Governor/Chancellor nominees, the Board of Management, Doctoral Research Committees, Board of Studies, advisory boards, faculty selection committees, and examination boards for government and private universities, institutions, and research laboratories. As a professional, he has collaborated with and visited several renowned organizations, including IIRS (ISRO), CSIR-IIP, NIH, WIHG, CPWD, NTPC, Bank of Maharashtra, and APS Lifetech.

R. R. Sinha received his doctorate in "Sampling Techniques" from the Department of Statistics at Banaras Hindu University, Varanasi, India in 2001. He is currently an Associate Professor in the Department of Mathematics at Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India. In addition to overseeing the dissertations of M. Tech. (Artificial Intelligence) and M. Sc. (Mathematics) candidates, he has guided two Ph. D. and three M. Phil. candidates. Dr. Sinha has more than 31 research publications and 3 book chapters published on various topics related to sampling techniques in national and international journals and proceedings. He has presented more than twenty-five research papers in international seminars/conferences/symposia and has been awarded the first prize for the best oral presentation of a research paper in an international conference. He is currently a life member of the Indian Society for Probability and Statistics, Indian Society of Agricultural Statistics, Indian Statistical Association, International Indian Statistical Association, and Ramanujan Mathematical Society. In addition to his scholarly responsibilities, Dr Sinha is an active member of editorial boards and a reviewer for esteemed journals and academic associations. His area of specialization is Sampling Theory, Data Analysis and Inference. ORCID identifier number of Dr. R. R. Sinha is 0000-0001-6386-1973.

Klappentext

In an era dominated by mathematical and statistical models, this book unravels the profound significance of these tools in decoding uncertainties within numerical, observational, and calculation-based data.

Inhalt

1. Goodness of Fit Based and Variable Selection in Non-parametric Measurement Error Model. 2. Bayesian Statistics with Applications in Cosmology. 3. An Improved Sufficient Bootstrapping. 4. A New Measure of Empirical Mode. 5. On the Distribution of a Busy Period for The Single Server Queue with Balking, Catastrophes and Repairs. 6. Studying the impact of feature importance and weighted aggregation in tackling process fairness. 7. Gaussian Mixture Model with Modified Hard EM Algorithm in Clustering Problems. 8. Impatient customers on an M/M/1 queueing system subjected to differentiated vacations. 9. Application of Error Correction Model (ECM) in stabilizing/adjusting fiscal burden post covid situation. 10. An inventory model with preserving environment for perishable items under learning effect