james-kuo / bayesian-robust-regression Goto Github PK

I implemented Gibbs Sampler in R to fit Bayesian student-t “Robust” regression with Jeffrey’s prior on a dataset and compared it with Gaussian linear regression with Jeffrey’s prior. Bayesian student-t regression uses the more fat-tailed t-distribution as sampling distribution to adress the outlier problem in Gaussian regression. Artificial noises are added to a dataset under different settings to compare the posterior distributions of parameters of the student-t and Gaussian model. Model performances and robustness of posterior distributions are compared.

Ting_Yuan_Kuo_Final_Project.pdf is the paper.

Analysis.R, Analysis2.R, and Analysis3.R are R codes which support the main Final Project.rmd markdown file.

AnalysisMain.RData, Corrupt.RData, Corrupt2.RData, and Corrupt2alt.RData contains the processed datasets.

I compared the robust and gaussian models on a dataset with only 1 independent variable so we can visualize.

Left panel is when the quadratic term is added in addition to the linear term, and right panel is when only the linear term is present. Lower theta value corresponds to a larger artificial outlier added to the dataset [1]. We can see that in the robust model, regression lines are much more stable no matter how large the outlier compared to the gaussian regression.

Shown below are the posterior distributions of the regression coefficient when only linear term is present. We can see as the outlier becomes "larger", posterior distribution of the robust model does not change by much, whereas posterior distribution of the gaussian model becomes very noisy.

1. By larger outlier I mean a larger exponentially distributed shock is added to high-leverage points in the data. theta is the rate parameter.