Theoretical Multivariate Statistics
(Master, 1st Semester, WS)
Tasks of Multivariate Statistics
Most of the observable phenomena in empirical sciences are of a multivariate nature. For example, in financial studies, assets in the stock markets are simultaneously analyzed. In medicine, recorded observations of subjects in different locations are the basis of reliable diagnostics and medications. In quantitative marketing, consumer preferences are collected in order to construct models of consumer behavior. This course will teach you a collection of tools and procedures that involve the observation and analysis of more than one statistical variable at a time.
We will treat the following topics: descriptive techniques, matrix algebra, regression analysis, simple analysis of variance, multivariate distributions, theory of the multinormal, theory of estimation, hypothesis testing, decomposition of data matrices by factors, principal components analysis.
 Descriptive Statistics and Tests are important tools to make conclusions about the sample and the population. We will repeat known descriptive measures and tests first and introduce new ones later on. A case study will be presented.
 Regression analysis attempts to determine a linear formula that can describe how some variables respond to changes in others. Linear regression is a method for determining the parameters of a linear system. In this course a case of multiple linear regression is studied, when there are more than one explanatory variable.
 Multivariate normal distribution, also sometimes called a multivariate Gaussian distribution, is a specific probability distribution, which is a generalization to higher dimensions of the onedimensional normal distribution. The importance of the normal distribution as a model of quantitative phenomena in the natural and behavioral sciences is due to the central limit theorem (the proof of which requires advanced undergraduate mathematics). Many psychological measurements and physical phenomena (like photon counts and noise) can be approximated well by the normal distribution. While the mechanisms underlying these phenomena are often unknown, the use of the normal model can be theoretically justified by assuming that many small, independent effects are additively contributing to each observation
 The theory of estimation develops the basic theoretical tools which are needed to derive estimators and to determine their properties in general situations. This part of the course mainly builds on the maximum likelihood theory. In many situations, the maximum likelihood estimator indeed shares asymptotic optimal properties which make its use easy and appealing. The multivariate normal population and the linear regression model will be presented, where the applications are numerous and derivations are easy to do. In multivariate setups, the maximum likelihood estimator is at times too complicated to be derived analytically. In such cases, the estimators are obtained using numerical methods (nonlinear optimization). However, the general theory and the asymptotic properties of this estimator remain simple and valid.
 Hypothesis testing is used to test the hypothesis that the unknown parameter belongs to some subspace. A rejection region can be based on a likelihood ratio principle. This technique is described by various testing problems and examples.
News
 Please register for the course in OPAL.
 For questions please contact Martin Waltz
Schedule
Event  Day  Time  Room  Teacher 

Lecture 
Monday 
2. DS 
POT 251 

Exercise 
Friday 
4. DS 
HÜL/S386/H  Martin Waltz 
Themen
 Matrix algebra.
 Moving to higher dimensions.
(regression analysis, simple analysis of variance)  Multivariate Distributions.
 Theory of Multinormal.
 Copula.
 Theory of Estimation.
 Hypothesis Testing.
Literatur

Backhaus, K., Erichson, B., Plinke, W., Weiber, R. (2008), Multivariate Analysemethoden: Eine anwendungsorientierte Einführung (12. Auflage), Springer Verlag.

Härdle, W., Simar, L. (2015), Applied Multivariate Statistical Analysis (4nd edititon), Springer Lehrbuch.

Härdle, W., Hlavka, Z. (2007), Multivariate Statistics: Exercises and Solutions, Springer Lehrbuch.