Fitting Mixtures of Erlangs to Censored and Truncated Data Using the EM Algorithm

2014 ◽  
Author(s):  
Katrien Antonio ◽  
Andrei L Badescu ◽  
Gong Lan ◽  
X. Sheldon Lin ◽  
Verbelen Roel
Keyword(s):  
Em Algorithm ◽  
Truncated Data ◽  
The Em Algorithm ◽  
Censored And Truncated Data

scholarly journals FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM

2015 ◽  
Vol 45 (3) ◽  
pp. 729-758 ◽  
Author(s):  
Roel Verbelen ◽  
Lan Gong ◽  
Katrien Antonio ◽  
Andrei Badescu ◽  
Sheldon Lin
Keyword(s):  
Em Algorithm ◽  
Simulated Data ◽  
Real Data ◽  
Data Sets ◽  
Truncated Data ◽  
The Em Algorithm ◽  
Loss Modeling ◽  
Censored And Truncated Data

AbstractWe discuss how to fit mixtures of Erlangs to censored and truncated data by iteratively using the EM algorithm. Mixtures of Erlangs form a very versatile, yet analytically tractable, class of distributions making them suitable for loss modeling purposes. The effectiveness of the proposed algorithm is demonstrated on simulated data as well as real data sets.

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

1992 ◽  
Vol 43 (1-2) ◽  
pp. 31-44 ◽  
Author(s):  
Peter N. Jones ◽  
Mclachlan J. Geoffrey
Keyword(s):  
Convergence Rate ◽  
Em Algorithm ◽  
Mixture Model ◽  
Truncated Data ◽  
The Em Algorithm

Fitting Mixture Models to Grouped and Truncated Data via the EM Algorithm

Biometrics ◽  
1988 ◽  
Vol 44 (2) ◽  
pp. 571 ◽  
Author(s):  
G. J. McLachlan ◽  
P. N. Jones
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Truncated Data ◽  
The Em Algorithm

EVM: Speeding Up the EM Algorithm

2020 ◽  
Author(s):  
Mark Britten-Jones
Keyword(s):  
Em Algorithm ◽  
The Em Algorithm

Estimation and identification of time-varying long-term fading channels via the particle filter and the EM algorithm

2011 ◽  
Author(s):  
Xiao Ma ◽  
Mohammed M. Olama ◽  
Seddik M. Djouadi ◽  
Charalambos D. Charalambous
Keyword(s):  
Em Algorithm ◽  
Particle Filter ◽  
Fading Channels ◽  
Time Varying ◽  
The Em Algorithm

On Detection of Improper Solutions in Factor Analysis by the EM Algorithm

2014 ◽  
Vol 41 (2) ◽  
pp. 245-268 ◽  
Author(s):  
Kentaro Hayashi ◽  
Lu Liang
Keyword(s):  
Factor Analysis ◽  
Em Algorithm ◽  
Improper Solutions ◽  
The Em Algorithm

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

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.

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