TY - JOUR
AU - WU, YING NIAN
AB - Abstract The EM algorithm and its extensions are popular tools for modal estimation but are often criticised for their slow convergence. We propose a new method that can often make EM much faster. The intuitive idea is to use a ‘covariance adjustment’ to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data. The way we accomplish this is by parameter expansion; we expand the complete-data model while preserving the observed-data model and use the expanded complete-data model to generate EM. This parameter-expanded EM, PX-EM, algorithm shares the simplicity and stability of ordinary EM, but has a faster rate of convergence since its M step performs a more efficient analysis. The PX-EM algorithm is illustrated for the multivariate t distribution, a random effects model, factor analysis, probit regression and a Poisson imaging model. © 1998 Biometrika Trust
TI - Parameter expansion to accelerate EM: The PX-EM algorithm
JF - Biometrika
DO - 10.1093/biomet/85.4.755
DA - 1998-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/parameter-expansion-to-accelerate-em-the-px-em-algorithm-1yTp6EAlRU
SP - 755
EP - 770
VL - 85
IS - 4
DP - DeepDyve
ER -