Probability and Statistical Inference
Chapman & Hall/CRC (Verlag)
978-0-367-74912-5 (ISBN)
Features:
•Complete introduction to mathematical probability, random variables, and distribution theory.
•Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes.
•Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference.
•Detailed introduction to Bayesian statistics and associated topics.
•Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC).
This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.
Miltiadis Mavrakakis obtained his PhD in Statistics at LSE under the supervision of Jeremy Penzer. His first job was as a teaching fellow at LSE, taking over course ST202 and completing this book in the process. He splits his time between lecturing (at LSE, Imperial College London, and the University of London International Programme) and his applied statistical work. Milt is currently a Senior Analyst at Smartodds, a sports betting consultancy, where he focuses on the statistical modelling of sports and financial markets. He lives in London with his wife, son, and daughter. Jeremy Penzer first post-doc job was as a research assistant at the London School of Economics. Jeremy went on to become a lecturer at LSE and to teach the second year statistical inference course (ST202) that formed the starting point for this book. While working at LSE, his research interests were time series analysis and computational statistics. After 12 years as an academic, Jeremy shifted career to work in financial services. He is currently Chief Marketing and Analytics Officer for Capital One Europe (plc). Jeremy lives just outside Nottingham with his wife and two daughters.
1. Introduction
2. Probability
3. Random Variables and Univariate Distributions
4. Multivariate Distributions
5. Conditional Distributions
6. Statistical Models
7. Sample Moments and Quantiles
8. Estimation, Testing, and Prediction
9. Likelihood-based Inference
10. Inferential Theory
11. Bayesian Inference
12. Simulation Methods
| Erscheinungsdatum | 07.09.2022 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Texts in Statistical Science |
| Zusatzinfo | 6 Tables, black and white; 63 Line drawings, black and white; 63 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 453 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| ISBN-10 | 0-367-74912-2 / 0367749122 |
| ISBN-13 | 978-0-367-74912-5 / 9780367749125 |
| Zustand | Neuware |
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