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Data-geometry and resampling-based inference for selecting predictors for monsoon precipitation

Banff International Research Station for Mathematical Innovation and Discovery

We will present a technique for studying the geometry of data-clouds, using multivariate quantiles and extremes. Using multivariate quantiles one can construct data-depth functions, which are rank-like functions that may be used for center-to-tails ordering of observations. Several robust inferential procedure can be based on data-depth functions and multivariate quantiles, and we will first discuss a few such techniques. Then, we present a method of using resampling coupled with data-depth functions, that can be used for consistently estimating the joint distribution of all parameter estimators under all candidate models, while simultaneously assigning a score to each candidate model. The model-score may be used for model evaluation and selection. The candidate models do not need to be nested within each other, and the number of parameters in each model as well as in the data generating process can grow with sample size. An illustrative example of prediction and obtaining the true physical forces driving Indian summer monsoon rainfall will be presented. This talk includes joint work with Lindsey Dietz, Megan Heyman, and Subho Majumdar.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: University of Minnesota

2018-04-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65692

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/