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Activity Number: 260
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #319994 View Presentation
Title: Big Data Algorithms for Rank-Based Estimation
Author(s): John Kapenga and John Kloke* and Joseph McKean
Companies: Western Michigan University and University of Wisconsin and Western Michigan University
Keywords: nonparametric ; linear models ; robust

Rank-based (R) estimation for statistical models is a robust nonparametric alternative to classical estimation procedures such as least squares. R methods have been developed for models ranging from linear models, to linear mixed models, to timeseries, to nonlinear models. Advantages of these R methods over traditional methods such as maximum-likelihood or least squares is that they require fewer assumptions, are robust to gross outliers, and are highly efficient at a wide range of distributions. The R package, Rfit, was developed to widely disseminate these methods as the software uses standard linear model syntax and includes commonly used functions for inference and diagnostic procedures. Large datasets are becoming coming in practice and the ability to obtain results in real time is desirable. We are developing algorithms for R estimation which improves the speed at the expense of a slight decrease in accuracy in big data settings. In this talk we describe the traditional as well as the big data algorithms for R estimation. We present examples and results from simulation studies which illustrate the algorithms.

Authors who are presenting talks have a * after their name.

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