The Minimum Distance Approach Chapman Hallcrc Monographs On Statistics Applied

Statistical inference is an essential tool for making sense of data and drawing s in various fields. One widely used approach is the Minimum Distance Method. In this article, we will explore the concept of minimum distance and its relevance in statistical analysis. Specifically, we will focus on the book "The Minimum Distance Approach" from the Chapman Hallcrc Monographs on Statistics Applied series.

Statistics is a discipline that deals with the collection, organization, analysis, interpretation, and presentation of data. The use of statistical methods enables researchers and analysts to draw meaningful insights from data, make predictions, and make informed decisions.

The Minimum Distance Approach, authored by leading statisticians in the field, provides a comprehensive guide to this specific statistical method. The book delves into the theory, principles, and applications of the minimum distance approach, making it an invaluable resource for statisticians, researchers, and students.

Statistical Inference: The Minimum Distance Approach (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book 120)

by Ayanendranath Basu(1st Edition, Kindle Edition)

4.6 out of 5

Language	:	English
File size	:	10522 KB
Screen Reader	:	Supported
Print length	:	429 pages

FREE

The minimum distance approach is a powerful statistical method that compares observed data with an underlying theoretical model. It aims to find parameter estimates that minimize the distance between the observed and expected values. This approach provides a framework for hypothesis testing, model selection, and parameter estimation.

The Chapman Hallcrc Monographs on Statistics Applied series aims to bring together cutting-edge research and practical applications in statistics. The series covers a wide range of topics, including regression analysis, time series analysis, multivariate analysis, and Bayesian statistics. The books in the series are written by experts in their respective fields and provide in-depth knowledge and insights into various statistical methods.

The Minimum Distance Approach monograph is a valuable addition to the series, as it focuses specifically on this important statistical technique. The book starts with an to the minimum distance approach and its underlying principles. It then provides step-by-step guidance on how to apply this approach to real-world data.

Throughout the book, the authors use examples and case studies to illustrate the application of the minimum distance approach. The theoretical concepts are presented in a clear and concise manner, making it accessible to both beginners and experienced statisticians.

One of the strengths of "The Minimum Distance Approach" is its focus on practical applications. The authors provide code snippets and examples in R and other statistical software to demonstrate how to implement the minimum distance approach in data analysis. This hands-on approach allows readers to gain a thorough understanding of the method and apply it to their own research.

Furthermore, the book covers recent developments and extensions of the minimum distance approach. It discusses advanced topics such as robust estimation, model misspecification, and nonparametric methods. These additions make it a comprehensive resource for statisticians interested in exploring the full potential of the minimum distance approach.

, "The Minimum Distance Approach" from the Chapman Hallcrc Monographs on Statistics Applied series is an essential resource for statisticians, researchers, and students interested in the minimum distance method. The book provides a detailed to the approach, its applications, and its extensions. By combining theoretical explanations with practical examples, it offers a comprehensive understanding of this important statistical technique. Whether you are a beginner or an experienced statistician, this monograph will undoubtedly enhance your knowledge and skills in statistical inference.

Statistical Inference: The Minimum Distance Approach (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book 120)

by Ayanendranath Basu(1st Edition, Kindle Edition)

4.6 out of 5

Language	:	English
File size	:	10522 KB
Screen Reader	:	Supported
Print length	:	429 pages

FREE

In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews developments in density-based minimum distance inference for independently and identically distributed data. Extensions to other more complex models are also discussed.

Comprehensively covering the basics and applications of minimum distance inference, this book introduces and discusses:

• The estimation and hypothesis testing problems for both discrete and continuous models

• The robustness properties and the structural geometry of the minimum distance methods

• The inlier problem and its possible solutions, and the weighted likelihood estimation problem

• The extension of the minimum distance methodology in interdisciplinary areas, such as neural networks and fuzzy sets, as well as specialized models and problems, including semi-parametric problems, mixture models, grouped data problems, and survival analysis.

Statistical Inference: The Minimum Distance Approach gives a thorough account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adjustment functions, graphical descriptions of robustness, penalized and combined distances, weighted likelihood, and multinomial goodness-of-fit tests. This carefully crafted resource is useful to researchers and scientists within and outside the statistics arena.