{"product_id":"mathematical-statistics-1","title":"Mathematical Statistics","description":"\u003cp\u003e\u003cb\u003ePresents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are uniquely based on the likelihood function\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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. \u003ci\u003eMathematical Statistics: An Introduction to Likelihood Based Inference\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eIn addition to the connected chapters mentioned above, \u003ci\u003eMathematical Statistics\u003c\/i\u003e 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. \u003c\/p\u003e \u003cul\u003e\n\u003cli\u003ePrepares students with the tools needed to be successful in their future work in statistics data science\u003c\/li\u003e\n\u003cli\u003eIncludes practical case studies including real-life data collected from Yellowstone National Park, the Donner party, and the Titanic voyage\u003c\/li\u003e\n\u003cli\u003eEmphasizes the important ideas to statistical modeling, such as sufficiency, exponential family distributions, and large sample properties\u003c\/li\u003e\n\u003cli\u003eIncludes sections on Bayesian estimation and credible intervals\u003c\/li\u003e\n\u003cli\u003eFeatures examples, problems, and solutions\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMathematical Statistics: An Introduction to Likelihood Based\u003c\/i\u003e \u003ci\u003eInference\u003c\/i\u003e is an ideal textbook for upper-undergraduate and graduate courses in probability, mathematical statistics, and\/or statistical inference.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":54217998205272,"sku":"9781118771044","price":121.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9781118771044_9a52b627-ee22-4b4f-bb89-bc6a2a9f87a0.jpg?v=1781145936","url":"https:\/\/agendabookshop.com\/products\/mathematical-statistics-1","provider":"Agenda Bookshop","version":"1.0","type":"link"}