Specifications
book-author | Richard J. Rossi |
---|---|
publisher | Wiley; 1st edition |
file-type | |
pages | 464 pages |
language | English |
asin | B07DS3WLG4 |
isbn10 | 1118771044 |
isbn13 | 9781118771044 |
Book Description
Presents a unified approach to parametric estimation; hypothesis testing; confidence intervals; and statistical modeling; which are uniquely based on the likelihood function. This ebook; Mathematical Statistics: An Introduction to Likelihood Based Inference (PDF); addresses mathematical statistics for first year graduate and upper-undergraduates students; tying chapters on estimation; hypothesis testing; confidence intervals; and statistical models together to present a unifying focus on the likelihood function. It also emphasizes the important ideas in statistical modeling; such as exponential family distributions; sufficiency; and large sample properties. Rossi’s Mathematical Statistics: An Introduction to Likelihood Based Inference PDF makes advanced topics accessible and understandable and covers many topics in more depth than typical mathematical statistics textbooks. It includes numerous case studies; great examples; 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 Poisson regression; non-iid observations; logistic regression; linear regression; and linear models.
- Features good examples; problems; and solutions
- Includes sections on Bayesian estimation and credible intervals
- Prepares college students with the tools needed to be successful in their future work in statistics data science
- Emphasizes the important ideas to statistical modeling; such as exponential family distribution; sufficiency; and large sample properties
- Includes practical case studies including real-life data collected from the Donner party; Yellowstone National Park; and the Titanic voyage
Mathematical Statistics: An Introduction to Likelihood Based Inference is an ideal etextbook for graduate and upper-undergraduate courses in mathematical statistics; probability; and/or statistical inference.
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