Applied Statistical Methods: ISGES 2020, Pune, India, January 2-4

This book collects select contributions from the International Conference on Importance of Statistics in Global Emerging (ISGES 2020), discussing recent developments in various areas of statistics with applications in small area estimation, Bayesian models, ranked set sampling, fuzzy supply chain, probabilistic supply chain models, dynamic Gaussian process models, grey relational analysis, and multi-item inventory models. It also includes worked examples and case studies to illustrate the applications of these methods.

Format: Paperback / softback
Length: 307 pages
Publication date: 15 April 2023
Publisher: Springer Verlag, Singapore

This book is a compilation of selected contributions presented at the International Conference on Importance of Statistics in Global Emerging (ISGES 2020), which took place at the Department of Mathematics and Statistics, University of Pune, Maharashtra, India, from January 2 to 4, 2020. The conference aimed to explore recent advancements in various fields of statistics, with a focus on their applications across a wide range of key topics. These topics included small area estimation techniques, Bayesian models for small areas, ranked set sampling, fuzzy supply chain, probabilistic supply chain models, dynamic Gaussian process models, grey relational analysis, and multi-item inventory models. Additionally, the potential use of other models, such as generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs, and different reliability models, was discussed.

The book provides detailed worked examples and case studies to illustrate the practical applications of recently developed statistical methods. This makes it an invaluable resource for applied statisticians, students, research project leaders, and practitioners from diverse marginal disciplines and interdisciplinary research.

The contributions in this book cover a wide range of topics, reflecting the interdisciplinary nature of the field. The authors, who are experts in their respective areas, present their research findings and discuss their implications for statistical practice and research. The book is organized into several chapters, each focusing on a specific area of statistics.

One of the key themes of the book is the use of small area estimation techniques, which are particularly relevant in the context of global emerging economies. The authors discuss various methods, such as the jackknife and bootstrap methods, for estimating population parameters in small areas, with a focus on their accuracy and efficiency. They also explore the application of these techniques in health research, economics, and other fields.

Another important topic covered in the book is Bayesian models for small areas. The authors discuss the advantages and disadvantages of Bayesian methods, as well as their implementation in statistical software. They present case studies and examples to illustrate the use of Bayesian models in various applications, such as disease mapping, environmental monitoring, and social science research.

Ranked set sampling is another technique that is gaining popularity in statistical research. The authors discuss the theory and application of ranked set sampling, particularly in the context of survey sampling. They present methods for designing ranked set samples, as well as their advantages and disadvantages compared to other sampling techniques. They also explore the use of ranked set sampling in health research, economics, and other fields.

Fuzzy supply chain management is a growing area of interest in the business world. The authors discuss the challenges and opportunities associated with fuzzy supply chain management, as well as the use of statistical methods to improve decision-making in this area. They present case studies and examples to illustrate the application of fuzzy supply chain models in various industries, such as manufacturing, logistics, and healthcare.

Probabilistic supply chain models are another important topic covered in the book. The authors discuss the development and application of probabilistic models to improve supply chain performance. They present methods for modeling supply chain risk, as well as their advantages and disadvantages compared to other risk management techniques. They also explore the use of probabilistic supply chain models in various industries, such as manufacturing, logistics, and healthcare.

Dynamic Gaussian process models are a powerful tool for modeling complex systems. The authors discuss the development and application of dynamic Gaussian process models in various fields, such as finance, engineering, and healthcare. They present methods for modeling time-dependent processes, as well as their advantages and disadvantages compared to other modeling techniques. They also explore the use of dynamic Gaussian process models in various applications, such as risk assessment, forecasting, and decision-making.

Grey relational analysis is a method for analyzing complex systems using graphical models. The authors discuss the development and application of grey relational analysis in various fields, such as finance, engineering, and healthcare. They present methods for analyzing multi-item systems, as well as their advantages and disadvantages compared to other analysis techniques. They also explore the use of grey relational analysis in various applications, such as risk assessment, forecasting, and decision-making.

Multi-item inventory models are a crucial tool for managing inventory in various industries. The authors discuss the development and application of multi-item inventory models in various industries, such as manufacturing, logistics, and healthcare. They present methods for modeling inventory systems, as well as their advantages and disadvantages compared to other inventory management techniques. They also explore the use of multi-item inventory models in various applications, such as demand forecasting, inventory optimization, and supply chain management.

In addition to these topics, the book also discusses the potential use of other models, such as generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs, and different reliability models. The authors discuss the advantages and disadvantages of these models, as well as their implementation in statistical software. They present case studies and examples to illustrate the application of these models in various fields, such as finance, engineering, and healthcare.

Overall, this book provides a comprehensive and up-to-date overview of recent developments in various areas of statistics with applications of a wide range of key topics. It is a valuable resource for applied statisticians, students, research project leaders
leaders, and practitioners from diverse marginal disciplines and interdisciplinary research.
This book is a comprehensive compilation of selected contributions presented at the International Conference on Importance of Statistics in Global Emerging (ISGES 2020), which took place
place at the Department of Mathematics and Statistics, University of Pune, Maharashtra, India, from January 2 to 4, 2020. The conference aimed to explore recent advancements in various fields of statistics, with a focus on their applications across a wide range of key topics. These topics included small area estimation techniques, Bayesian models for small areas, ranked set sampling, fuzzy supply chain, probabilistic supply chain models, dynamic Gaussian process models, grey relational analysis, and multi-item inventory models. Additionally, the potential use of other models, such as generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs, and different reliability models, was discussed.

The book provides detailed worked examples and case studies to illustrate the practical applications of recently developed statistical methods. This makes it an invaluable resource for applied statisticians, students, research project leaders, and practitioners from diverse marginal disciplines and interdisciplinary research.

The contributions in this book cover a wide range of topics, reflecting the interdisciplinary nature of the field. The authors, who are experts in their respective areas, present their research findings and discuss their implications for statistical practice and research. The book is organized into several chapters, each focusing on a specific area of statistics.

One of the key themes of the book is the use of small area estimation techniques, which are particularly relevant in the context of global emerging economies. The authors discuss various methods, such as the jackknife and bootstrap methods, for estimating population parameters in small areas, with a focus on their accuracy and efficiency. They also explore the application of these techniques in health research, economics, and other fields.

Another important topic covered in the book is Bayesian models for small areas. The authors discuss the advantages and disadvantages of Bayesian methods, as well as their implementation in statistical software. They present case studies and examples to illustrate the use of Bayesian models in various applications, such as disease mapping, environmental monitoring, and social science research.

Ranked set sampling is another technique that is gaining popularity in statistical research. The authors discuss the theory and application of ranked set sampling, particularly in the context of survey sampling. They present methods for designing ranked set samples, as well as their advantages and disadvantages compared to other sampling techniques. They also explore the use of ranked set sampling in health research, economics, and other fields.

Fuzzy supply chain management is a growing area of interest in the business world. The authors discuss the challenges and opportunities associated with fuzzy supply chain management, as well as the use of statistical methods to improve decision-making in this area. They present case studies and examples to illustrate the application of fuzzy supply chain models in various industries, such as manufacturing, logistics, and healthcare.

Probabilistic supply chain models are another important topic covered in the book. The authors discuss the development and application of probabilistic models to improve supply chain performance. They present methods for modeling supply chain risk, as well as their advantages and disadvantages compared to other risk management techniques. They also explore the use of probabilistic supply chain models in various industries, such as manufacturing, logistics, and healthcare.

Dynamic Gaussian process models are a powerful tool for modeling complex systems. The authors discuss the development and application of dynamic Gaussian process models in various fields, such as finance, engineering, and healthcare. They present methods for modeling time-dependent processes, as well as their advantages and disadvantages compared to other modeling techniques. They also explore the use of dynamic Gaussian process models in various applications, such as risk assessment, forecasting, and decision-making.

Grey relational analysis is a method for analyzing complex systems using graphical models. The authors discuss the development and application of grey relational analysis in various fields, such as finance, engineering, and healthcare. They present methods for analyzing multi-item systems, as well as their advantages and disadvantages compared to other analysis techniques. They also explore the use of grey relational analysis in various applications, such as risk assessment, forecasting, and decision-making.

Multi-item inventory models are a crucial tool for managing inventory in various industries. The authors discuss the development and application of multi-item inventory models in various industries, such as manufacturing, logistics, and healthcare. They present methods for modeling inventory systems, as well as their advantages and disadvantages compared to other inventory management techniques. They also explore the use of multi-item inventory models in various applications, such as demand forecasting, inventory optimization, and supply chain management.

In addition to these topics, the book also discusses the potential use of other models, such as generalized Lindley shared frailty models, Benktander Gibrat risk model, decision-consistent randomization method for SMART designs, and different reliability models. The authors discuss the advantages and disadvantages of these models, as well as their implementation in statistical software. They present case studies and examples to illustrate the application of these models in various fields, such as finance, engineering, and healthcare.

Overall, this book provides a comprehensive and up-to-date overview of recent developments in various areas of statistics with applications of a wide range of key topics. It is a valuable resource for applied statisticians, students, research project leaders, and practitioners from diverse marginal disciplines and interdisciplinary research.

Weight: 504g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811679346
Edition number: 1st ed. 2022