Objective Bayesian analysis for the accelerated degradation model using Wiener process with measurement errors

Figures & data

Table 1. MSEs and coverage probabilities (within parentheses) of the Bayesian estimators and MLE.

	MLE/priors	σ		θ
(5,5,5)	MLE	0.751	0.401	0.344
		(0.8032)	(0.8128)	(0.8308)	(0.8214)
		0.737	0.317	0.299
		(0.9378)	(0.9382)	(0.9618)	(0.9348)
		0.728	0.308	0.298
		(0.9380)	(0.9382)	(0.9612)	(0.9392)
(10,15,15)	MLE	0.1487	0.1256
		(0.8426)	(0.8662)	(0.8628)	(0.8618)
		0.1347	0.1082
		(0.9406)	(0.9588)	(0.9432)	(0.9412)
		(0.9412)	(0.9532)	(0.9464)	(0.9426)
(20,20,20)	MLE
		(0.8672)	(0.8904)	(0.9018)	(0.8846)
		(0.9442)	(0.9468)	(0.9548)	(0.9534)
		(0.9524)	(0.9512)	(0.9498)	(0.9508)
(25,25,25)	MLE
		(0.8892)	(0.9124)	(0.9248)	(0.9024)
		(0.9468)	(0.9518)	(0.9512)	(0.9518)
		(0.9488)	(0.9504)	(0.9502)	(0.9508)

Table 2. Posterior means and 95% credible intervals (within parentheses) of parameters.

Prior	Parameter	Mean	95% Credible interval
	θ	1.4802
	σ
	θ	1.4765
	σ