The Big Data Blog

My interests and what you will find here

Big Data

Nonparametric Statistics

Functional Data

Some informations

About the Author

Curriculum vitae

Heiko Wagner

Bonn

Hello and Welcome at thebigdatablog.com. My name is Heiko Wagner and this Website is about stuff which is somewhat related to my research. However, this website has a different focus. While my research is mostly about theoretical properties of various estimators, this website is focussed on implementation details, in particular if we are facing a large amount of data. Beside I also want to present different projects I carried out in my spare time to a larger audience.

Publications:

Heiko Wagner and Alois Kneip “Nonparametric Registration to Low-Dimensional Functionspaces” published in Computational statistics & data analysis 2019, Vol. 138, 49-63. R Code
Maria Grith, Heiko Wagner, Wolfgang K. Härdle and Alois Kneip “Functional Principal Component Analysis for Derivatives of Multidimensional Curves” published in Statistica Sinica 2018 Vol. 28, 2469-2496. Matlab Code
Dominik Poß and Heiko Wagner “Analysis of juggling data: Registering data to principal components to explain amplitude variation” published in the Electronic Journal of Statistics 2014, Vol. 8, No. 2, 1825-1834.

Coding Skills:

Python , Matlab, R, SQL, PL/SQL, Oracle Apex , C++, Java, JavaScript, Pascal, PHP, CSS, Unity3d, Cuda and others

Conference Participations:

CMStatistics 2016, Sevilla Spain, Dec 2016
Annual Meeting of the SSC 13, Edmonton Canada, May 2013
CTW: Statistics of Time Warpings and Phase Variations, Columbus USA, Nov 2012
Workshop on Neuroeconomics at CENs, Bonn Germany, 2010

Peer-Reviewed Journal Articles:

Biometrics, 2019, 2020

Notable non scientific projects:

Engineering, Programming: Tunisian “Wahl-O-Mat” tunivote.net (PHP, MySQL, Javascript, Ajax)
Idea, Engineering, Programming: add-ons.de (PHP,MySQL)
Engineering, Programming: Exams database for statistic department Bonn (Oracle, Apex, PL/SQL)

Reccomandations by the author

Books i like

The Elements of Statistical Learning

This book gives a good overview at various machine learning techniques. It lacks of a deeper theoretical foundation and proofs, but can be considered as the best entry point for beginners.

Introductory Real Analysis

Still my favorite math book. This book requires no prior knowledge and is a very good introduction of the underling basic concepts that eventually lead to probability theory.

Markov Chains

A clear and mathematically exact introduction to markov chains.

Functional Data Analysis

An excellent and fun to read introduction into the field of functional data analysis. Lack of proofs and a deeper theoretical foundation, also perhaps a bit outdated, but still one of the best books to start with.

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