Parameter Estimation of Incomplete Data in Competing Risks Using the EM Algorithm

2005 ◽  
Vol 54 (2) ◽  
pp. 282-290 ◽  
Author(s):  
C. Park
Keyword(s):  
Parameter Estimation ◽  
Em Algorithm ◽  
Competing Risks ◽  
Incomplete Data ◽  
The Em Algorithm

scholarly journals PENDUGAAN DATA HILANG DENGAN METODE YATES DAN ALGORITMA EM PADA RANCANGAN LATTICE SEIMBANG

2015 ◽  
Vol 4 (2) ◽  
pp. 74
Author(s):  
MADE SUSILAWATI ◽  
KARTIKA SARI
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Basic Concept ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Animal Husbandry ◽  
Lattice Design ◽  
The Em Algorithm ◽  
Missing Data Estimation ◽  
Data Estimation

Missing data often occur in agriculture and animal husbandry experiment. The missing data in experimental design makes the information that we get less complete. In this research, the missing data was estimated with Yates method and Expectation Maximization (EM) algorithm. The basic concept of the Yates method is to minimize sum square error (JKG), meanwhile the basic concept of the EM algorithm is to maximize the likelihood function. This research applied Balanced Lattice Design with 9 treatments, 4 replications and 3 group of each repetition. Missing data estimation results showed that the Yates method was better used for two of missing data in the position on a treatment, a column and random, meanwhile the EM algorithm was better used to estimate one of missing data and two of missing data in the position of a group and a replication. The comparison of the result JKG of ANOVA showed that JKG of incomplete data larger than JKG of incomplete data that has been added with estimator of data. This suggest  thatwe need to estimate the missing data.

Parameter Estimation of the Reduced RUM Using the EM Algorithm

2013 ◽  
Vol 38 (2) ◽  
pp. 137-150 ◽  
Author(s):  
Yuling Feng ◽  
Brian T. Habing ◽  
Alan Huebner
Keyword(s):  
Parameter Estimation ◽  
Em Algorithm ◽  
The Em Algorithm
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