Principles of Statistical Analysis (Paperback)

Ery Arias-Castro

ISBN 10: 1108747442 / ISBN 13: 9781108747448

Edité par Cambridge University Press, Cambridge, 2022

A propos de cet article

Paperback. This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance simulation and sampling, as well as experimental design and data collection that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference. This concise course in principled data analysis for the mathematically literate uses survey sampling and designed experiments as a foundation for statistical inference. Covering essentials for advanced undergraduates and selected topics typically taught at the graduate level, its 700 problems many computational build understanding and skills. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9781108747448

Détails bibliographiques

• Titre: Principles of Statistical Analysis (Paperback)
• Éditeur: Cambridge University Press, Cambridge
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 1108747442
• ISBN à 13 chiffres: 9781108747448
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

À propos de l?auteur

Ery Arias-Castro is a professor in the Department of Mathematics and in the Halıcıoğlu Data Science Institute at the University of California, San Diego, where he specializes in theoretical statistics and machine learning. His education includes a bachelor's degree in mathematics and a master's degree in artificial intelligence, both from École Normale Supérieure de Cachan (now École Normale Supérieure Paris-Saclay) in France, as well as a Ph.D. in statistics from Stanford University in the United States.