Nonparametric spectral analysis with missing data via the EM algorithm

2005 ◽  
Vol 15 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Yanwei Wang ◽  
Petre Stoica ◽  
Jian Li ◽  
Thomas L. Marzetta
Keyword(s):  
Spectral Analysis ◽  
Missing Data ◽  
Em Algorithm ◽  
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.

scholarly journals The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data

2016 ◽  
Vol 12 (1) ◽  
pp. 65-77
Author(s):  
Michael D. Regier ◽  
Erica E. M. Moodie
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Em Algorithm ◽  
Optimal Solution ◽  
Bias Reduction ◽  
Model Parameters ◽  
Finite Sample ◽  
Imperfect Data ◽  
The Em Algorithm ◽  
Finite Sample Properties

Abstract We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

Multi-User Detection Analysis via Aitken-δ2 EM Acceleration Algorithm

2011 ◽  
Vol 225-226 ◽  
pp. 284-288
Author(s):  
Jin Long Xian ◽  
Jian Wu Li
Keyword(s):  
Missing Data ◽  
Em Algorithm ◽  
Iterative Algorithm ◽  
Convergence Speed ◽  
Speed Of Convergence ◽  
Detection Analysis ◽  
The Em Algorithm ◽  
Aitken Method

The EM iterative algorithm is commonly used in recent years for missing data, which has the character of easy and popular applicability. But the EM algorithm has a fatal weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Aitken method is proposed in order to solve this problem.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance, and compared its speed of convergence with the EM algorithm that Aitken-acceleration algorithm has faster convergence than the standard EM algorithm, and we also illustrate the performance of simulation.

EM Acceleration Algorithm Using the Vector-ε Apply in Multi-User Detection

2011 ◽  
Vol 225-226 ◽  
pp. 280-283
Author(s):  
Jin Long Xian ◽  
Jian Wu Li
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Em Algorithm ◽  
Iterative Algorithm ◽  
Computational Efficiency ◽  
Convergence Speed ◽  
Speed Of Convergence ◽  
The Em Algorithm

The EM iterative algorithm is a very general and popular algorithm that commonly used for missing data to find maximum likelihood in recent years, which has the character of stability,flexibility and simlicity.However, the EM algorithm has a great weakness that the convergence speed is slowly; Acceleration of the EM algorithm using the Vector-ε method is proposed in order to solve this problem in this paper.In Multi-user Detection, via this accelerated algorithm, we get a good performance which trends to ML performance and improving the computational efficiency, and compared its speed of convergence with the EM algorithm that Vector-ε acceleration algorithm has faster convergence than the standard EM algorithm.

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