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Application of EM algorithm on missing categorical data analysis

Hasan, Noraini (2009) Application of EM algorithm on missing categorical data analysis. Masters thesis, Universiti Teknologi Malaysia, Faculty of Science.



Expectation- Maximization algorithm, or in short, EM algorithm is one of the methodologies for solving incomplete data problems sequentially based on a complete framework. The EM algorithm is a parametric approach to find the Maximum Likelihood, ML parameter estimates for incomplete data. The algorithm consists of two steps. The first step is the Expectation step, better known as E-step, finds the expectation of the loglikelihood, conditional on the observed data and the current parameter estimates; say . The second step is the Maximization step, or Mstep, which maximize the loglikelihood to find new estimates of the parameters. The procedure alternates between the two steps until the parameter estimates converge to some fixed values.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains (Matematik)) - Universiti Teknologi Malaysia, 2009; Supervisor : Assoc. Prof. Dr. Ismail Mohamad
Uncontrolled Keywords:expectation-maximization algorithms, estimation theory, missing observations, statistics
Subjects:Q Science > QA Mathematics
ID Code:12403
Deposited By: Zalinda Shuratman
Deposited On:31 May 2011 01:26
Last Modified:13 Sep 2017 08:18

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