Maximum Likelihood Estimation in Graphical Models with Missing Values

Didelez, V. and Pigeot, Iris (1997): Maximum Likelihood Estimation in Graphical Models with Missing Values. Collaborative Research Center 386, Discussion Paper 75

Abstract

In this paper we discuss maximum likelihood estimation when some observations are missing in mixed graphical interaction models assuming a conditional Gaussian distribution as introduced by Lauritzen&Wermuth (1989). For the saturated case ML estimation with missing values via the EM algorithm has been proposed by Little&Schluchter (1985). We expand their results to the special restrictions in graphical models and indicate a more efficient way to compute the E--step. The main purpose of the paper is to show that for certain missing patterns the computational effort can considerably be reduced.

• Item Type: Paper (Research Paper)
• Subjects: Mathematics, Computer Science and Statistics; Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386
• Dewey Classification: 600 Natural sciences and mathematics; 600 Natural sciences and mathematics > 510 Mathematics