Stochastic Neighborhood Embedding (pdf)
MVA Multivariate Statistical Analysis
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- 49 Students Enrolled
MVA Multivariate Statistical Analysis
Lectures for Multivariate Statistical Analysis
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- 0 Rating
- 0 Reviews
- 49 Students Enrolled
Course Content
21 courselets
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2.4 M |
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Uniform Manifold Approximation and Projection (pdf)
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2.04 M |
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MVA - Boxplots, Histograms, Kernel Densities (video)
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16 min | |
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MVA - Boxplots, Histograms, Kernel Densities (pdf)
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2.34 M |
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MVA - Scatterplots, Faces, Curves and PCPs (pdf)
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30.52 M | |
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MVA - Scatterplots, Faces, Curves and PCPs (video)
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9 min |
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MVA Matrixes Eigenvectors Decompositions (video)
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30 min | |
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MVA Matrixes Eigenvectors Decompositions (pdf)
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1.87 M |
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MVA Quadtratic Forms and Geometry (pdf)
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1.43 M | |
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MVA Quadtratic Forms and Geometry (video)
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36 min |
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MVA - Covariance Correlation and Summary (video)
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11 min | |
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MVA - Covariance Correlation and Summary (pdf)
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4.54 M |
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MVA - t-Tests, Linear Regression (pdf)
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1.52 M | |
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MVA - t-Tests, Linear Regression (video)
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8 min |
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MVA - Multivariate Distrubutions 1 (pdf)
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11.01 M | |
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MVA - Multivariate Distrubutions 1 (video)
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41 min |
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MVA - Multivariate Distrubutions 2 (video)
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39 min | |
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MVA - Multivariate Distrubutions 2 (pdf)
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11.01 M |
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MVA - Multivariate Distrubutions 3 (pdf)
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11.01 M | |
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MVA - Multivariate Distrubutions 3 (video)
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32 min |
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MVA Theory of the multinormal (pdf)
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1.78 M | |
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MVA Theory of the multinormal (video)
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37 min |
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MVA Theory of estimation (pdf)
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1.42 M |
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MVA Hypothesis Testing (pdf)
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1.81 M |
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MVA - Variable Selection (pdf)
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2.59 M | |
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MVA - Variable Selection (video)
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6 min |
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MVA - Decomposition of Data Matrices (pdf)
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2.06 M | |
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MVA - Decomposition of Data Matrices (video)
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10 min |
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MVA Principal Components (pdf)
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4.09 M | |
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MVA Principal Components (video)
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15 min |
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MVA Data Mining, Proximity Measures (pdf)
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6.81 M | |
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MVA Data Mining, Proximity Measures (video)
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21 min |
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MVA Cluster Analysis (pdf)
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24.61 M | |
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MVA Cluster Analysis (video)
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20 min |
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MVA - Canonical Correlation Analysis (video)
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16 min | |
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MVA - Canonical Correlation Analysis (pdf)
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2.06 M |
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Locally Linear Embeddings (pdf)
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8.18 K | |
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Locally Linear Embeddings (video)
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18 min |
Requirements
- none
General Overview
Description
Lectures based on the book: Härdle & Simar & Fengler : Multivariate Statistical Analysis. The course presents multivariate statistical analysis in a comprehensive way, including the most useful standard approaches to multi-dimensional data. It also features numerous examples and provides the underlying links to the sourcecodes in R, Python or SAS code, allowing readers to reproduce all computations.
Recommended for you
Meet the instructors !
About the Instructor
Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn. He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”. He directs IDA Institute for Digital Assets,
University of Economic Studies, Bucharest, RO. His research focuses on data analytics, dimension reduction and quantitative finance. He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the www.quantlet.com platform, a cryptocurrency index, CRIX www.royalton-crix.com He is 玉山学者 （Yushan Scholar), web page hu.berlin/wkh
About the Instructor
Phd Student @ HU-Berlin
Research Interest
- Natural Language Processing
- Explainable AI
- Research Reproducibility
About the Instructor
Matthias Fengler is a full professor of econometrics at the University of St. Gallen, specializing in financial econometrics. He holds a PhD in Quantitative Finance from Humboldt Universität zu Berlin and has experience as both a quantitative analyst and academic. His research focuses on asset pricing, volatility modeling, and risk management. He has published in leading journals such as the Journal of Econometrics, Journal of Financial Econometrics, and Journal of Banking and Finance, and co-authored the sixth edition of Applied Multivariate Statistical Analysis (Springer). His projects include the award-winning “Monitoring Consumption Switzerland,” and his recent work applies machine learning and alternative data, including textual analysis, to financial forecasting.