Table 3.
J = 30 |
J = 45 |
|||||||
---|---|---|---|---|---|---|---|---|
ρ | n | MCMC | MH-RM | B MH-RM | MCMC | MH-RM | B MH-RM | |
Simple Q | .2 | 1,000 | 0.076 | 0.149 | 0.149 | 0.080 | 0.154 | 0.152 |
2,000 | 0.057 | 0.109 | 0.111 | 0.059 | 0.101 | 0.102 | ||
.5 | 1,000 | 0.082 | 0.190 | 0.184 | 0.084 | 0.183 | 0.182 | |
2,000 | 0.057 | 0.143 | 0.141 | 0.056 | 0.133 | 0.129 | ||
.75 | 1,000 | 0.081 | 0.276 | 0.269 | 0.081 | 0.236 | 0.230 | |
2,000 | 0.061 | 0.207 | 0.189 | 0.057 | 0.200 | 0.187 | ||
Complex Q | .2 | 1,000 | 0.227 | 0.248 | 0.237 | 0.269 | 0.248 | 0.237 |
2,000 | 0.178 | 0.198 | 0.194 | 0.194 | 0.202 | 0.196 | ||
.5 | 1,000 | 0.319 | 0.294 | 0.292 | 0.259 | 0.283 | 0.264 | |
2,000 | 0.184 | 0.241 | 0.238 | 0.201 | 0.227 | 0.219 | ||
.75 | 1,000 | 0.429 | 0.405 | 0.422 | 0.354 | 0.371 | 0.338 | |
2,000 | 0.369 | 0.439 | 0.500 | 0.300 | 0.410 | 0.363 |
Note. MCMC = Markov chain Monte Carlo; MH-RM = Metropolis–Hastings Robbins–Monro; B MH-RM = Bayesian Metropolis–Hastings Robbins–Monro.