(NMST611) Advanced Statistical Seminar
Wednesday: 15:40 - 17:20 | @K1 (online ZOOM stream)
Aktuálne
- Vzhľadom k aktuálnym pandemickým obmedzeniam bude seminár (minimálne po dobu nutnú) prebiehať výhradne v online réžii. Ak to okolnosti dovolia, bude možné sledovať prednášky aj formou streamovaného prenosu priamo do posluchárne K1. Technické riešenie by malo umožniť aj plnohodnotné zapojenie do následnej diskusie.
- Harmonogram jednotlivých prednášok, zoznam hostí, názov prednášky, aj stručný abstrakt budú postupne zverejnované na tejto webovej stránke.
Rozvrh / Seminar schedule
- 03.03.2021 | cancelled -------------------------------------------------------------------------------------------
- 17.03.2021 | 16:00 | Emanuele Del Fava
Max Planck Institute for Demographic Research, Germany
Modeling international migration flows in Europe by integrating multiple data sources
- Migration has become a significant source of population change at the global level, with broad societal implications. Although understanding the drivers of migration is critical to enacting effective policies, theoretical advances in the study of migration processes have been limited by the lack of data on flows of migrants, or by the fragmented nature of these flows. In the talk, we build on existing Bayesian modeling strategies to develop a statistical framework for integrating different types of data on migration flows. We offer estimates, as well as associated measures of uncertainty, for immigration, emigration, and net migration flows among 31 European countries, by combining administrative and household survey data from 2002 to 2018. Substantively, we document the historical impact of the EU enlargement and the free movement of workers in Europe on migration flows. Methodologically, our approach improves on the Integrated Modeling of European Migration (IMEM) framework by providing a robust statistical framework for evaluating recent migration trends that is flexible enough to be further extended to incorporate new data sources, like social media.
- 31.03.2021 | 15:40 | Karel Hron
Univerzita Palackého v Olomouci, Česká Republika
Orthogonal decomposition and analysis of bivariate densities
in Bayes spaces
A new orthogonal decomposition for bivariate probability densities embedded in Bayes Hilbert spaces will be presented. It allows one to represent a density into independent and interactive parts, the former being built as the product of revised definitions of marginal densities and the latter capturing the dependence between the two random variables being studied. The developed framework opens new perspectives for dependence modelling (which is commonly performed through copulas), and allows for the analysis of dataset of bivariate densities, in a Functional Data Analysis perspective.
------------------------------------------------------------------------------------------- - 14.04.2021 | 15:40 | Richard Samworth
University of Cambridge, UK
USP: An independence test that improves on Pearson's chi-squared
and the G-test
We introduce the U-Statistic Permutation (USP) test of independence in the context of discrete data displayed in a contingency table. Either Pearson's chi-squared test of independence, or the Generalised Likelihood Ratio test (G-test), are typically used for this task, but we argue that these tests have serious deficiencies, both in terms of their inability to control the size of the test, and their power properties. By contrast, the USP test is guaranteed to control the size of the test at the nominal level for all sample sizes, has no issues with small (or zero) cell counts, and is able to detect distributions that violate independence in only a minimal way. The test statistic is derived from a U-statistic estimator of a natural population measure of dependence, and we prove that this is the unique minimum variance unbiased estimator of this population quantity. In the last quarter of the talk, I will show how this is a special case of a much more general methodology and theory for independence testing.
------------------------------------------------------------------------------------------- - 28.04.2021 | 15:40 | Roger Koenker
University College London, UK
Quantile Regression Remixed
Some recent computational and theoretical developments have significantly expanded the understanding and applicability of quantile regression methods. After a brief discussion of conformal quantile regression inference and prediction, Romano, Y., Patterson, E. and Candès, E. (2019) and convolution type smoothing methods for computation, by He, X., Pan, X., Tan, K. M. and Zhou, W.-X. (2020), the talk will focus on the proposed semiparametric efficient quantile regression estimator of Wang, Z. and K. Chen and Y. Lin and Z. Ying (2020).
------------------------------------------------------------------------------------------- - 26.05.2021 | 15:40 | Ivor Cribben
University of Alberta School of Business, Canada
Select change point methods for multivariate time series networks
Identifying change points in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. In this talk I will present two new change point detection methods. The first method presents each network snapshot of the data as a linear object and finds its respective univariate characterization via local and global network topological summaries. We adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of the change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. The second method uses non-negative matrix factorization, an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points. We apply our methods to simulated data and to real data, particularly, functional magnetic resonance imaging (fMRI) data sets.
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