Robust Bayesian nonparametric variable selection for linear regression

Spike‐and‐slab and horseshoe regressions are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real‐world statistics a...

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Veröffentlicht in:	Stat (International Statistical Institute) 2024-06, Vol.13 (2), p.n/a
Hauptverfasser:	Cabezas, Alberto, Battiston, Marco, Nemeth, Christopher
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Bayesian analysis Dirichlet problem Dirichlet process heteroskedasticity horseshoe Machine learning Outliers (statistics) Probabilistic models Regression models spike‐and‐slab variable selection
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