An Introduction to Probability and Statistical Inference (2版)

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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

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書名：Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction 作者：Imbens 出版社：CAMBRIDGE 出版日期：2015/04/06 ISBN：9780521885881

MATHEMATICAL STATISTICS: AN INTRODUCTION TO LIKELIHOOD BASED INFERENCE

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【簡介】 Presents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are uniquely based on the likelihood function This book addresses mathematical statistics for upper-undergraduates and first year graduate students, tying chapters on estimation, confidence intervals, hypothesis testing, and statistical models together to present a unifying focus on the likelihood function. It also emphasizes the important ideas in statistical modeling, such as sufficiency, exponential family distributions, and large sample properties. Mathematical Statistics: An Introduction to Likelihood Based Inference makes advanced topics accessible and understandable and covers many topics in more depth than typical mathematical statistics textbooks. It includes numerous examples, case studies, a large number of exercises ranging from drill and skill to extremely difficult problems, and many of the important theorems of mathematical statistics along with their proofs. In addition to the connected chapters mentioned above, Mathematical Statistics covers likelihood-based estimation, with emphasis on multidimensional parameter spaces and range dependent support. It also includes a chapter on confidence intervals, which contains examples of exact confidence intervals along with the standard large sample confidence intervals based on the MLE's and bootstrap confidence intervals. There’s also a chapter on parametric statistical models featuring sections on non-iid observations, linear regression, logistic regression, Poisson regression, and linear models. Prepares students with the tools needed to be successful in their future work in statistics data science Includes practical case studies including real-life data collected from Yellowstone National Park, the Donner party, and the Titanic voyage Emphasizes the important ideas to statistical modeling, such as sufficiency, exponential family distributions, and large sample properties Includes sections on Bayesian estimation and credible intervals Features examples, problems, and solutions Mathematical Statistics: An Introduction to Likelihood Based Inference is an ideal textbook for upper-undergraduate and graduate courses in probability, mathematical statistics, and/or statistical inference. 【目錄】

Probability and Statistical Inference (10版)

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【簡介】 Advances in computing technology, particularly in science and business, have increased the need for more statistical scientists to examine the huge amount of data being collected. Written by veteran statisticians, Probability and Statistical Inference, 10th Edition is an authoritative introduction to an in-demand field. It emphasizes the existence of variation in almost every process, and how the study of probability and statistics helps us understand this variation. This applied overview of probability and statistics reinforces basic mathematical concepts with numerous real-world examples and applications to illustrate the relevance of key concepts. A good calculus background is needed, but no previous study of probability or statistics is required. It is designed for a 2-semester course, but also can be adapted for a 1-semester course.  【目錄】 Ch 1 Probability Ch 2 Discrete Distributions Ch 3 Continuous Distributions Ch 4 Bivariate Distributions Ch 5 Distributions of Functions of Random Variables Ch 6 Point Estimation Ch 7 Interval Estimation Ch 8 Tests of Statistical Hypotheses Ch 9 More Tests