This page contains the material for the course on High Dimensional Statistics given at UH as part of the Msc programme in Mathematics and Statistics.

The course is lectured every second year. The next course will be given in period II of 2021-2022, that is, in Nov-Dec 2021. Register in Sisu.

Lecture notes and exercises are by Matti Pirinen and distributed under CC-BY-SA license. The slides contain individual figures from literature, as clearly indicated on each slide, and their copyright belongs to the original publisher.

Topic | Notes | Slides |
---|---|---|

0. Motivation | ||

0.1. Linear model | html pdf Rmd | |

0.2. Linear model in practice | html pdf Rmd | |

Large scale inference | ||

1. Multiple testing problem | html pdf Rmd | |

2. False discovery rate | html pdf Rmd | |

3. Q-value | html pdf Rmd | |

4. Bayesian inference | html pdf Rmd | |

Regression | ||

5. Multiple regression | html pdf Rmd | |

6. Penalized regression | html pdf Rmd | |

6.1. Breast cancer example | html pdf Rmd | |

7. Bootstrap & Inference | html pdf Rmd | |

8. Bayesian variable selection | html pdf Rmd | |

Dimension reduction | ||

9. PCA & SVD | html pdf Rmd | |

10. t-SNE & UMAP | html pdf Rmd | |

Summary | ||

Summary & Exam |

Excellent and freely available books covering these topics, and many others, include

- Introduction to Statistical Learning by G.James, D.Witten, T.Hastie, R.Tibshirani.
- Elements of Statistical Learning by T.Hastie, R.Tibshirani, J.Friedman.
- Computer Age Statistical Inference by B.Efron, T.Hastie.
- Statistical Learning with Sparsity by T.Hastie, R.Tibshirani, M. Wainwright.

Contact: matti.pirinen'at'helsinki.fi

Updated: 2-Nov-2021