Introduction to Mathematical Statistics using R

Institut: Stochastik u. Wirtschaftsmathematik
Autor: Werner Gurker
ISBN: 9783903024809
Seitenanzahl: 554
Herausgeber: TU Verlag
Erscheinungsort: Wien 38.000025

EUR 38,00

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Art.Nr. PLU 142

Introduction to Mathematical Statistics using R

Introduction to Mathematical Statistics using R

Institut: Stochastik u. Wirtschaftsmathematik
Autor: Werner Gurker
ISBN: 9783903024809
Seitenanzahl: 554
Herausgeber: TU Verlag
Erscheinungsort: Wien


Introduction to Mathematical Statistics using R
This is a text for beginning students of statistics who would like to understand the theory behindsome of the most common statistical procedures used in applications. Emphasis is laid on themathematical but also on the computational aspects of statistics. Throughout we make use of the statistical software R and provide working R-code for the examples in the text and for some of the problems. (The data sets and R-scripts can be downloaded from the website of the TUVerlag.)
In Sections 1 to 6 we discuss the fundamentals of probability theory as far as they are needed in statistics. Beginning with Section 7 we introduce the framework of statistical modeling and inference, mainly from a classical but also from a Bayesian perspective.
Apart from various empirical and graphical procedures, we discuss the basics of estimation and hypothesis testing, methods for constructing confidence intervals and some methods of nonparametric statistical inference. Likelihood methods which are central in parametric statistical inference, are discussed in some detail. However, we also touch on some more advanced topics such as sufficiency, and discuss numerical and simulation techniques such as the EM algorithm and MCMC methods. We conclude with a section on linear models, in particular linear regression and ANOVA models.

The main topics are:
Probability Models – Random Variables – Bivariate Random Vectors – Multivariate Distributions
– Some Special Distributions – Limit Theorems – Sample Statistics – Statistical Inference –
Likelihood Methods – Bayesian Inference – Nonparametric Procedures – Linear Models

Werner Gurker


Werner Gurker is Ass.Prof.i.R. at the Institute of Statistics and Mathematical Methods in Eco- nomics of TU Wien, member of the research group Applied Statistics (ASTAT). For many years he gave various courses on Regression and Calibration Analysis, Statistical Process Control, Reli- ability Analysis, and on other topics from Applied and Mathematical Statistics. He has gained a rich experience in teaching statistical methods and their applications based on R.

 

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