An Introduction to Probability Theory and Mathematical Statistics by Vijay K. RohatgiA well-balanced introduction to probability theory and mathematical statistics
Featuring a comprehensive update, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided into three parts, the Third Edition begins by presenting the fundamentals and foundations of probability. The second part addresses statistical inference, and the remaining chapters focus on special topics.
Featuring a substantial revision to include recent developments, An Introduction to Probability and Statistics, Third Edition also includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked-out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate- level students majoring in probability and statistics.
Probability Theory and Mathematical Statistics
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It seems that you're in Germany. We have a dedicated site for Germany. These proceedings of the fifth joint meeting of Japanese and Soviet probabilists are a sequel to Lecture Notes in Mathematics Vols. They comprise 61 original research papers on topics including limit theorems, stochastic analysis, control theory, statistics, probabilistic methods in number theory and mathematical physics. An accuracy of Gaussian approximation of sum distribution of independent random variables in Banach spaces. Non-linear filtering of stochastic processes and optimal signal transmission through a feedback channel. The domain of attraction of a non-gaussian self-similar process with finite variance.