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71.0 MB
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316AF1A0C91BC367A7596DEBB56F974537594536
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July 25, 2025, 12:55 p.m.
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(Last updated: July 25, 2025, 12:56 p.m.)
| File | Size |
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| Ferreira D. Computational Statistics in Java 2ed 2025.pdf | 71.0 MB |
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| Uploaded by andryold1 | Size 71.0 MB | Health [ 34 /27 ] | Added 2025-07-25 |
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SOURCE: Ferreira D. Computational Statistics in Java 2ed 2025
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MEDIAINFO
Textbook in PDF format In this book, we aimed to address the topic of computational statistics, which is so important for the scientific community and, especially, for students in graduate programs in statistics. We can confidently state that computational statistics has become, and currently is, one of the main areas of statistics. Moreover, the knowledge in this area can be, and often is, applied in other fields of statistics, engineering, and physics. Bayesian inference is a typical example where a computational approach is generally used. When we conceived this book, we had many doubts about what to cover and how to approach each chosen topic. Thus, we opted to write a book that would allow the reader to go beyond a simple set of instructions, without getting lost in a maze of proofs. On the other hand, we sought to present models and methods in a comprehensive and non-restrictive manner. Another motivation that guided and encouraged us to develop this project was the experience we gained through research that utilizes the concepts and methods of computational statistics. With accumulated experience and knowledge in this area, we felt the need to share and disseminate everything we had learned so far with our students, avoiding the hedonism that often plagues professors and researchers. We also did this thinking about the personal benefit, which we cannot deny, that it would bring us by directly engaging with the vast literature in this branch of statistics. However, we do not intend to cover all topics in this book, nor do we aim to exhaust all possibilities for any specific topic. On the contrary, we hope this book serves as an introduction to computational statistics and motivates undergraduate and graduate students in statistics to delve deeper into this field. Wishing to deepen our knowledge of the wonderful programming language Java, we sought to write numerous methods and classes for the various topics covered in this work. We hope this book becomes a good option for professionals in computational statistics and can also be adopted in undergraduate and graduate statistics courses as a textbook for computational statistics. In this way, our objective will have been achieved. Knowledge of a programming language is essential to efficiently use computational statistics techniques. However, it is not limiting. Currently, various programs, such as SAS and R, are particularly interesting because they provide various computational tools already implemented in an optimized way. Thus, routines for generating random numbers, matrix operations, obtaining cumulative distribution functions, among others, can be easily used with simple commands. However, we will rarely mention these programs in this book, as we wish to present and unveil what each of these routines in these programs does. The Java language was chosen to meet this objective. We also considered the good processing speed of routines written in this language and the portability of the language. We also chose to use the NetBeans program to implement the routines written in Java. Java is an excellent high-level language that allows us to work with high numerical precision. Thus, we will assume that the reader already has some prior knowledge of the Java language. Introduction Generation of Uniform Random Samples Samples of Continuous Random Variables Samples of Discrete Random Variables Special Functions Discrete Probability Distributions Continuous Probability Distributions Non-Central Probability Distributions Matrix and Vector Algebra Samples of Multidimensional Random Variables Descriptive Statistics Computationally Intensive Statistical Methods Bootstrap Methods Order Statistics and Multiple Comparisons Bibliographic References
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