document: Calibrated initials for an EM applied to recursive models of categorical variables The estimates from an EM , when it is applied to a large causal model of 10 or more categorical variables , are often subject to the initial values for the estimates . This phenomenon becomes more serious as the model structure becomes more complicated involving more variables . As a measure of compensation for this , it has been recommended in literature that EMs are implemented several times with different sets of initial values to obtain more appropriate estimates . We propose an improved approach for initial values . The main idea is that we use initials that are calibrated to data . A simulation result strongly indicates that the calibrated initials give rise to the estimates that are far closer to the true values than the initials that are not calibrated
doc_bio_tags: B I O O B O O B I O B I O O O O B O O O O O O O B I I O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: em recursive models categorical variables calibrated initials large causal model initial values simulation