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Applied Multivariate Statistical Analysis Short course Faculty of Economics & Business, University of Zagreb, Croatia Wolfgang Karl Härdle 11.04 - 15.04.2016 APPLIED MULTIVARIATE STATISTICAL ANALYSIS 1 Wolfgang Karl Härdle Wolfgang Karl Härdle completed his Dr. rer. nat. in Mathematics at Heidelberg University and received his habilitation in Economics at Friedrich Wilhelm Universität Bonn. He was the founder and Director of Collaborative Research Center CRC 373 “Quantification and Simulation of Economic Processes” (1994 - 2003) and also the Director of C.A.S.E. (Center for Applied Statistics and Economics) (2001 - 2014). He is currently the Director of CRC 649 “Economic Risk” (2005 - 2016) and the SinoGerman International Research Training Group IRTG1792 “High dimensional non stationary time series analysis” (2013-2018). He has been teaching Master courses at Ladislaus von Bortkiewicz Chair of Statistics at Humboldt-Universität zu Berlin for more than twenty years. His research focuses on dimension reduction techniques, computational statistics and quantitative finance. He has published 30 books and more than 250 papers in top statistical, econometrics and finance journals and is one of the “Highly Cited Scientist” according to the Institute for Scientific Information. He is among the top 1% of economists registered at REPEC and has similar top notch rankings in other scales, such as the Handelsblatt ranking. His professional experience includes financial engineering, structured product design and credit risk analysis. He currently focuses his research on crypto currencies and DEDA Digital Economy & Decision Analytics. He has supervised more than 40 PhD students and is holding up long-term research relations to partners in the USA, Singapore, Prague, Warsaw, Paris, Cambridge, Beijing, Xiamen and Taipei among others. Course Contents APPLIED MULTIVARIATE STATISTICAL ANALYSIS 2 Descriptive Statistics and Tests are important tools to make conclusions about the sample and the population. Descriptive measures and known test will be repeated and new descriptive measures and tests will be introduced. A case study will be presented. Factor analysis is a statistical data reduction technique used to explain variability among observed random variables in terms of fewer unobserved random variables called factors. The observed variables are modelled as linear combinations of the factors, plus "error" terms. The analysis will isolate the underlying factors that explain the data. For factor specification, principal component analysis or common factor analysis can be used. Canonical correlation analysis tries to establish whether or not there are linear relationships among two sets of variables (covariates and response). It searches vectors a and b such that the random variables a'X and b'Y maximize the correlation. A significant part of the course is devoted to data mining techniques. Classification and Regression Trees (CART) classifies the data to predefined classes using so-called decision trees. By asking only yes/no, question dataset is split always into two subgroups. The process is then repeated for each of the resulting subsets until a desired size of the tree is reached. Support Vector Machines (SVM) goes further than CART and splits the data with non-linear decision rule. SVM has showed itself as an efficient tool for credit scoring and insolvency analysis. Schedule All examples are presented in R. The Quantlets are available here: www.quantlet.de APPLIED MULTIVARIATE STATISTICAL ANALYSIS 3 Day 1 Descriptive Statistics and PCA 17:00 - 20:00 Correlation, Dependence PCA Principal Component Analysis Factor Identification Day 2 Cluster Analysis 17:00 - 20:00 Proximity between Data Objects Cluster Algorithms Support Vector Machines Day 3 Discriminant Analysis 17:00 - 20:00 Allocation Rules Practical Discrimination Rules Multidimensional scaling Day 4 Applications 17:00 - 20:00 Chi-square Decomposition Practical Correspondence Analysis Financial Applications, LASSO Contact Ladislaus von Bortkiewicz Chair of Statistics C.A.S.E. - Center for Applied Statistics & Economics School of Business and Economics APPLIED MULTIVARIATE STATISTICAL ANALYSIS 4 Humboldt-Universität zu Berlin Unter den Linden 6 10099 Berlin, Germany Telefone +49 30 2093-5631 FAX +49 30 2093-5649 E-Mail [email protected] Links https://www.wiwi.huberlin.de/de/professuren/quantitativ/statistik http://sfb649.wiwi.hu-berlin.de https://www.wiwi.hu-berlin.de/de/forschung/irtg http://crix.hu-berlin.de http://sfb649.wiwi.hu-berlin.de/fedc/data.php http://sfb649.wiwi.hu-berlin.de/frm/index.html http://quantlet.de APPLIED MULTIVARIATE STATISTICAL ANALYSIS 5