Published two textbooks. The first book is or beginners in Statistics, Probability and Data Science, and the second one is for modellers of spatio-temporal data.
Abstract
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners.
The book comprises 19 chapters divided into five parts. Part I introduces basic statistics and the R software package, teaching readers to calculate simple statistics and create basic data graphs. Part II delves into probability concepts, including rules and conditional probability, and introduces widely used discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random variables. Part III explores statistical inference, covering point and interval estimation, hypothesis testing, and Bayesian inference. This part is intentionally less technical, making it accessible to readers without an extensive mathematical background. Part IV addresses advanced probability and statistical distribution theory, assuming some familiarity with (or concurrent study of) mathematical methods like advanced calculus and linear algebra. Finally, Part V focuses on advanced statistical modelling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applicable to various data and decision analytics contexts.
Based on years of teaching experience, this textbook includes numerous exercises and makes extensive use of R, making it ideal for year-long data science modules and courses. In addition to university courses, the book amply covers the syllabus for the Actuarial Statistics 1 examination administered by the Institute and Faculty of Actuaries in London. It also provides a solid foundation for postgraduate studies in statistics and probability, or a reliable reference for statistics.
date: “2022-03-02T00:00:00Z” doi: ""
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publication: Published by Chapman and Hall. Here is a Twitter video introduction. For more info please go to the book page.
abstract: Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modeling as modeling automatically aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modeling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems.
Keeping the applied scientists in mind, this book presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. However, the presentation in the book does not lose sight of mathematical and statistical rigor as it presents the underlying theories of Bayesian inference and computation in stand alone chapters in the first part which would be appealing to mathematics/statistics major final year undergraduate or post-graduate students who are in search of such modeling.
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summary: This is a textbook for advanced undergraduates and post-graduate students. All my other publications are listed on my publication page.
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Sujit Sahu
Professor of Statistics
My research interests include Bayesian modeling and computation, modeling of spatial and spatio-temporal data.