Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers

Figures & data

Fig. 1 Bayesian LMS of blood pressure data.

Fig. 2 Marginal posterior densities with different n, k, and degree of contamination.

Fig. 3 RMSE of parameters (β) and ρ.

Fig. 4 Marginal posterior densities of ρ.

Fig. 5 Marginal posteriors of LMS, LTS and ARMA.

Table 1 Posterior model probabilities for ARMA(p,q), portfolio 1, posterior LMS results.

$q\downarrow$	$p\to$	0	1	2	3	4
0		0.00	0.119	0.1801	0.118	0.000
1		0.000	0.480	0.0079	0.000	0.000
2		0.000	0.040	0.023	0.031	0.000
3		0.000	0.000	0.000	0.000	0.000
4		0.000	0.000	0.000	0.000	0.000

Fig. 6 BMA posteriors of α and ρ.