2009Mağallaẗ al-tarbiyaẗ wa-al-ʻilmOpen access

Estimating parameters of factor analysis model maximum likelihood method)) by using EM algorithm with application

Thanoon alshakerchy

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Abstract

Expectation maximization algorithm (EM) is used to create estimator with the same qualities of maximum likelihood Estimator taking into consideration the existence of two types of data, Data viewing (observed data) and hidden data (missing data), in this research the estimating parameters of factor analysis model (maximum likelihood method) has been done by using expectation maximization algorithm and applied factor analysis (maximum likelihood method) on data for patients infected with breast cancer, and found from the results importance all of the variables in breast cancer variables except first variable (level of education) and fifth variable (hormone treatment used).

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Expectation maximization algorithm (EM) is used to create estimator with the same qualities of maximum likelihood Estimator taking into consideration the existence of two types of data, Data viewing (observed data) and hidden data (missing data), in this research the estimating parameters of factor analysis model (maximum likelihood method) has been done by using expectation maximization algorithm and applied factor analysis (maximum likelihood method) on data for patients infected with breast cancer, and found from the results importance all of the variables in breast cancer variables except first variable (level of education) and fifth variable (hormone treatment used).

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Available abstract

Expectation maximization algorithm (EM) is used to create estimator with the same qualities of maximum likelihood Estimator taking into consideration the existence of two types of data, Data viewing (observed data) and hidden data (missing data), in this research the estimating parameters of factor analysis model (maximum likelihood method) has been done by using expectation maximization algorithm and applied factor analysis (maximum likelihood method) on data for patients infected with breast cancer, and found from the results importance all of the variables in breast cancer variables except first variable (level of education) and fifth variable (hormone treatment used).

Key concepts: Expectation–maximization algorithm, Maximum likelihood, Statistics, Estimator, Restricted maximum likelihood, Mathematics, Maximum likelihood sequence estimation, Missing data

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