This book, Statistical Modeling and Applications on Real-Time Problems shows how mathematics and statistical models are vital in today’s ever-complicated problems and uncertainties in different forms of data. These models are needed for business and regulatory decisions in both the public and private domains, ranging from climate analysis to production.
Statistical Modeling and Applications on Real-Time Problems: This book provides a complete, detailed guide to statistical modeling and optimization tools, along with the contributions of worldwide scholars. Ten chapters are remarkable for the diversity of issues discussed, including non-parametric goodness-of-fit statistics, Bayesian aspects, new resampling techniques, and more advanced measures for empirical modes. It includes the discussion of unboundedness and migration of customers in queueing systems and the application of Bayesian statistical methods.
Closed with an analysis of an inventory model of perishable goods, the book incorporates preservation technology as well as learning effects to establish the optimal order quantity. This book, Statistical Modeling and Applications on Real-Time Problems, will provide a pool of statistical and mathematical models which can be used when solving real-life problems as well as initiating further development of new approaches and debating on the current ones.
Statistical Modeling and Applications on Real-Time Problems Table of Contents:
- Goodness of Fit and Variable Selection in Non-Parametric Measurement Error Models
- Bayesian Statistics with Applications in Cosmology
- An Improved Sufficient Bootstrapping
- A New Measure of Empirical Mode
- Distribution of a Busy Period for Single Server Queues with Balking, Catastrophes, and Repairs
- Impact of Feature Importance and Weighted Aggregation in Tackling Process Fairness
- Gaussian Mixture Model with Modified Hard EM Algorithm in Clustering Problems
- Impatient Customers in an M/M/1 Queueing System with Differentiated Vacations
- Application of Error Correction Model (ECM) in Stabilizing Fiscal Burden Post-COVID
- Inventory Model for Perishable Items with Environmental Preservation and Learning Effects
Who is this course for?
- Data scientists and analysts
- Statisticians and mathematicians
- Scholars and Educationalists in Statistics and Mathematics
- Population involves students who are studying statistical modeling as well as optimization.
- Authorities, employees specialized in climate science and working in a manufacturing industry
- Anyone who wants to learn how to do statistical modeling and solve different problems of real life.
