Improving the convergence rate of the em algorithm for a mixture model fitted to grouped truncated data

Abstract

A method is provided for computing the standard errors for estimated parameters of a normal mixture model fitted to grouped truncated data. An estimate of the information matrix is obtained in terms of quantities computed during an implementation of the EM algorithm. This estimated information matrix is also used to enhance the convergence rate of the EM algorithm using a Newton-type step procedure. A comparison is made of this enhanced procedure with the original procedure using two sets of data each involving a two component mixture, one having mixing proportions almost equal, and the other with the mixing proportions in a ratio close to four to one.

Keywords:

• EM algorithm
• convergence rate
• information matrix
• mixture model
• grouped truncated data

^†Address for correspondence: CSIRO Biornetrics Unit, 306 Carmody Rd., St. Lucia Qld. 4067,Australia.

^†Address for correspondence: CSIRO Biornetrics Unit, 306 Carmody Rd., St. Lucia Qld. 4067,Australia.

Notes

^†Address for correspondence: CSIRO Biornetrics Unit, 306 Carmody Rd., St. Lucia Qld. 4067,Australia.