Modern Trends in Statistical Modeling: Stochastic Orders, Mixture Models, and Reliability Analysis

Title: Modern Trends in Statistical Modeling: Stochastic Orders, Mixture Models, and Reliability Analysis

Editors: Dr. Raju Bhakta; Mr. Parth Surendra Bhoyar

Publisher: Cogniverse Press, Nakari Gaon, Borigaon Siding, Jorhat-1, Assam, India

Edition: First Edition, August 2026

DOI: https://doi.org/10.5281/zenodo.22041777

ISBN: 978-81-68073-05-0 (Print Edition)

e-ISBN: 978-81-68073-06-7 (Digital Edition)

Peer Reviewed: Yes

Cover Designing: Cogniverse Press Digital Team

Copyright: © Authors

• Dr. Raju Bhakta – Assistant Professor, Mathematics and Basic Sciences, NIIT University, Neemrana, Alwar, Rajasthan-301705, India
• Mr. Parth Surendra Bhoyar – Research Student, Computer Science Engineering, NIIT University, Neemrana, Alwar, Rajasthan-301705, India

Published by: Cogniverse Press, Jorhat, Assam, India

Address: Nakari Gaon, Borigaon Siding, Jorhat-1

Phone: +91-9101730579

Website: cogniversepress.com

Email: cogniversepress@gmail.com

The views, interpretations, and research findings presented in this publication are solely of the respective authors. The publisher bears no responsibility or liability for the accuracy, completeness, or validity of the content herein.

The rapid evolution of statistical science over the past few decades has transformed the way uncertainty is modelled, interpreted, and applied across diverse disciplines. Advances in stochastic modeling, reliability theory, mixture distributions, Bayesian inference, extreme value analysis, and data-driven methodologies have significantly expanded the theoretical and practical frontiers of modern statistics. These developments have become increasingly relevant in engineering, finance, healthcare, environmental science, artificial intelligence, and many other interdisciplinary domains where complex systems and uncertain phenomena are central.

The primary objective of this edited volume is to provide a comprehensive platform for recent theoretical developments, methodological innovations, and contemporary applications in statistical modeling. The volume brings together contributions from researchers, academicians, and practitioners working in stochastic orders, order statistics, finite mixture models, reliability and survival analysis, lifetime distributions, Bayesian methods, copula theory, applied probability, and statistical applications in data science and engineering.

One of the distinguishing features of this volume is its interdisciplinary perspective. Statistical modeling today intersects with machine learning, computational methods, optimization, risk assessment, and decision sciences. The chapters address both rigorous mathematical frameworks and application-oriented problems, making the volume relevant to mathematics, statistics, engineering, computer science, operations research, and allied disciplines.

The book explores stochastic ordering techniques, finite mixture distributions, reliability theory and system modeling, survival and lifetime analysis, statistical inference for stochastic models, Bayesian reliability analysis, dependence structures through copula models, and the growing role of statistical methods in artificial intelligence, data science, and engineering.

The editors express their gratitude to all contributing authors for their scholarly work and to the reviewers and subject experts for their careful evaluation and constructive suggestions. The editors also acknowledge Cogniverse Press for its professional support throughout the editorial and publication process.

It is hoped that this book will stimulate further research, foster interdisciplinary collaboration, and encourage innovative applications of statistical modeling in contemporary scientific and technological problems.

Dr. Raju Bhakta
Editor-in-Chief
Mathematics and Basic Sciences
NIIT University, Neemrana, Rajasthan-301705, India

Mr. Parth Surendra Bhoyar
Co-Editor
Computer Science Engineering
NIIT University, Neemrana, Rajasthan-301705, India

• Stochastic orders and order statistics
• Extreme value theory
• Finite mixture models
• Reliability and survival analysis
• Lifetime distributions
• Bayesian reliability analysis
• Copula-based dependence modeling
• Applied probability
• Statistical modeling in data science and engineering
• Statistical and AI-based applications