## Nicolai Meinshausen: Catalogue data in Autumn Semester 2018 |

Name | Prof. Dr. Nicolai Meinshausen |

Field | Statistics |

Address | Professur für Statistik ETH Zürich, HG G 23.2 Rämistrasse 101 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 32 74 |

meinshausen@stat.math.ethz.ch | |

URL | http://stat.ethz.ch/~nicolai |

Department | Mathematics |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

401-3620-68L | Student Seminar in Statistics: Statistical Learning with Sparsity Number of participants limited to 24. Mainly for students from the Mathematics Bachelor and Master Programmes who, in addition to the introductory course unit 401-2604-00L Probability and Statistics, have heard at least one core or elective course in statistics. Also offered in the Master Programmes Statistics resp. Data Science. | 4 credits | 2S | M. Mächler, M. H. Maathuis, N. Meinshausen, S. van de Geer | |

Abstract | We study selected chapters from the 2015 book "Statistical Learning with Sparsity" by Trevor Hastie, Rob Tibshirani and Martin Wainwright. (details see below) | ||||

Objective | During this seminar, we will study roughly one chapter per week from the book. You will obtain a good overview of the field of sparse & high-dimensional modeling of modern statistics. Moreover, you will practice your self-studying and presentation skills. | ||||

Content | (From the book's preface:) "... summarize the actively developing field of statistical learning with sparsity. A sparse statistical model is one having only a small number of nonzero parameters or weights. It represents a classic case of “less is more”: a sparse model can be much easier to estimate and interpret than a dense model. In this age of big data, the number of features measured on a person or object can be large, and might be larger than the number of observations. The sparsity assumption allows us to tackle such problems and extract useful and reproducible patterns from big datasets." For presentation of the material, occasionally you'd consider additional published research, possibly e.g., for "High-Dimensional Inference" | ||||

Lecture notes | Website: with groups, FAQ, topics, slides, and Rscripts : https://stat.ethz.ch/lectures/as18/seminar.php#course_materials | ||||

Literature | Trevor Hastie, Robert Tibshirani, Martin Wainwright (2015) Statistical Learning with Sparsity: The Lasso and Generalization Monographs on Statistics and Applied Probability 143 Chapman Hall/CRC ISBN 9781498712170 Access : - https://www.taylorfrancis.com/books/9781498712170 (full access via ETH (library) network, if inside ETH (VPN)) - Author's website (includes errata, updated pdf, data): https://web.stanford.edu/~hastie/StatLearnSparsity/ | ||||

Prerequisites / Notice | We require at least one course in statistics in addition to the 4th semester course Introduction to Probability and Statistics, as well as some experience with the statistical software R. Topics will be assigned during the first meeting. | ||||

401-4619-67L | Advanced Topics in Computational StatisticsDoes not take place this semester. | 4 credits | 2V | N. Meinshausen | |

Abstract | This lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling. | ||||

Objective | Students learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes. | ||||

Content | The main focus will be on graphical models in various forms: Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models | ||||

Prerequisites / Notice | We assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics. | ||||

401-4623-00L | Time Series Analysis | 6 credits | 3G | N. Meinshausen | |

Abstract | Statistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R. | ||||

Objective | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | ||||

Content | This course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations. Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R. | ||||

Lecture notes | Not available | ||||

Literature | A list of references will be distributed during the course. | ||||

Prerequisites / Notice | Basic knowledge in probability and statistics | ||||

401-5620-00L | Research Seminar on Statistics | 0 credits | 2K | L. Held, T. Hothorn, D. Kozbur, M. H. Maathuis, N. Meinshausen, S. van de Geer, M. Wolf | |

Abstract | Research colloquium | ||||

Objective | |||||

401-5640-00L | ZüKoSt: Seminar on Applied Statistics | 0 credits | 1K | M. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer | |

Abstract | About 5 talks on applied statistics. | ||||

Objective | See how statistical methods are applied in practice. | ||||

Content | There will be about 5 talks on how statistical methods are applied in practice. | ||||

Prerequisites / Notice | This is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web: http://stat.ethz.ch/events/zukost Course language is English or German and may depend on the speaker. |