TBASS: A Robust Adaptation of Bayesian Adaptive Spline Surfaces

Technical Report · Mon Oct 05 00:00:00 EDT 2020

DOI:https://doi.org/10.2172/1671069· OSTI ID:1671069

Shen, Andy Anda ^[1]; Rumsey, Kelin Norman ^[1]; Francom, Devin Craig ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

The R package TBASS is an extension of the BASS package created by Francom and Sansó (2019). The package is used to fit a Bayesian multivariate adaptive spline to a dataset that either follows a Student’s t-distribution or has outliers. Much of the framework for TBASS is adapted from the concepts of Bayesian Multivariate Adaptive Regression Splines (BMARS), specifically the work done by Denison, Mallick, and Smith (1998). The spline function is fit using a Reversible-Jump Markov Chain Monte Carlo algorithm,. By including this more robust generalization, a dataset with outliers can be accurately fit using the BMARS model, without the possibility of overfitting or variance inflation.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: 89233218CNA000001
• OSTI ID: 1671069
• Report Number(s): LA-UR--20-27873
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Bayesian inference
nonparametric regression
robust regression
sensitivity analysis
splines