Download Wolfgang Karl Härdle

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
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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
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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
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Day 1
Descriptive Statistics and PCA
17:00 - 20:00
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Correlation, Dependence
PCA Principal Component Analysis
Factor Identification
Day 2
Cluster Analysis
17:00 - 20:00
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Proximity between Data Objects
Cluster Algorithms
Support Vector Machines
Day 3
Discriminant Analysis
17:00 - 20:00
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Allocation Rules
Practical Discrimination Rules
Multidimensional scaling
Day 4
Applications
17:00 - 20:00
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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
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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
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