## Applied Multivariate Statistical AnalysisFocusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: - A new chapter on Variable Selection (Lasso, SCAD and Elastic Net)
- All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de.
The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg. |

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### Contents

Part II Multivariate Random Variables | 51 |

Part III Multivariate Techniques | 250 |

Part IV Appendix | 555 |

572 | |

577 | |

### Other editions - View all

Applied Multivariate Statistical Analysis Wolfgang Karl Härdle,Léopold Simar No preview available - 2015 |

Applied Multivariate Statistical Analysis Wolfgang Karl Härdle,Léopold Simar No preview available - 2015 |

### Common terms and phrases

algorithm analysis applied approximation assets bank notes calculated called classification clusters coefficients column components Compute conditional Consider constraints coordinates copula correlation corresponding covariance covariance matrix data set defined denotes density dependence derive determined dimension direction discriminant distance distribution effect eigenvalues eigenvectors elements equal estimate Example Exercise explained factor function given gives groups Hence Housing hypothesis independent indices individuals interested interpretation Lasso linear lower marginal maximises mean measure method multivariate Multivariate Statistical node normal observations obtain original parameters plot points population positive presented probability problem projection provides random variable regions regression relation represent representation respect rule sample shows solution space squares statistic Summary Suppose Table technique Theorem transformation tree variables variance vector weight zero