Content
- Introduction
- Recovery model
- Monster series model
- Extension of the classical linear model
- General structure of the line
- Recovery model
- Mixed model
- Sometimes healing
- Self-construction
- Quantile regression
- Matrix Algebra
- Probability calculation and statistical presentation
- Normal distribution
- Conclusion
- Bayesian inference
- Bibliography
- Phonebook
Preface
Regression is a statistical procedure frequently and extensively used in the fields of social science, economics, and biological sciences to assess empirical situations.
In a similar vein, conventional linear regression and contemporary semi-nonparametric models are just two examples of the many distinct types of models and empirical methods that are available. The literature that is now available devotes the majority of its attention to certain regression models; nevertheless, these models differ substantially in terms of methodology, statistical level, and theoretical direction or applicability. Why is there a need for yet another regression book? Students and practitioners in a variety of applications can choose from a large number of introductory publications; nevertheless, these books only provide the background information.
Conversely, most texts focus on modern techniques that differ from semiparametric methods. On the other hand, these methods effectively convey to the reader that they offer advantages in both theory and practice, based on extremely high-level statistics. As a result, these books do not cater to mature readers who require the use of this strategy.
In an effort to bridge the gap between theory and practice, the purpose of this book is to use and integrate regression, non-parametric, and semi-parametric regression approaches.
This book presents fundamental sampling and regression methods in a practical and informative manner. Practical examples and case studies explain the methodology. We selected and provided the method on the basis of the availability of software that was user-friendly. We hold the belief that the advancement of applied research and the development of statistical methods, spurred by new challenges from various collaborations, require a link and equilibrium between theory and practice. Ruppert, Wand, and Carroll (2003) used a methodology similar to ours in their book Semiparametric Regression, albeit with different objectives. Our book primarily targets students, professors, and practitioners in the fields of social, economic, and health sciences. Additionally, our target audience includes individuals enrolled in statistical programs, mathematicians, and computer scientists interested in statistical modeling and data analysis. Based on the assumption that the reader has just a fundamental understanding of probability, mathematics, and statistics, we write it at an intermediate level of mathematical proficiency.
Brief text segments that start with an apostrophe and end with a comma serve as excerpts to provide details or more information. It is possible to omit these passages from the initial reading without compromising the consistency of the story. The box summarizes the most important features and details. Two appendices describe the essential matrix algebra, as well as the elements of probability and arithmetic calculations.
Depending on your interest, you can read the book’s chapters separately or in the following order:
Chapter 2 presents an introductory overview of logistic, nonparametric, and semiparametric regression methods. However, this chapter purposefully excludes technical knowledge and empirical material.
You can introduce the model by reading Chapters 1-4.
• You can begin studying the Mixed Verses (chapters 7.1-7.4) immediately following the fourth chapter and prior to the fifth and sixth chapters.
Immediately following the reading of Sections 1 through 4, go ahead and read Sections 10.1 and 10.2.
Reading chapters 1 through 4, chapters 7 through 7, and chapters 8 through 10 will help you understand semiparametric regression for continuous variables, which includes semiparametric quantile regression. You can also read chapters 8 through 10.
• Chapters 1 through 6 present regression models for continuous and discrete variables.
The following table presents a summary of the possible values measured (the sections enclosed in brackets).
Download For Free in PDF Format