M
Mortaza Jamshidian
Researcher at California State University, Fullerton
Publications - 41
Citations - 1901
Mortaza Jamshidian is an academic researcher from California State University, Fullerton. The author has contributed to research in topics: Missing data & Expectation–maximization algorithm. The author has an hindex of 18, co-authored 40 publications receiving 1733 citations. Previous affiliations of Mortaza Jamshidian include University of California, Berkeley & University of Isfahan.
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Acceleration of the EM Algorithm by using Quasi‐Newton Methods
TL;DR: It is proposed to approximate the inverse of the observed information matrix by using auxiliary output from the new hybrid accelerator and a numerical evaluation of these approximations indicates that they may be useful at least for exploratory purposes.
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ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines
TL;DR: The sensitivity of the ML estimates as well as the mean imputed and listwise deletion estimates to missing data mechanisms is investigated using three artificial data sets that are missing completely at random, missing at random (MAR), and neither MCAR nor MAR.
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MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)
TL;DR: The package MissMech implements two tests of MCAR that can be run using a function called TestMCARNormality, which is valid if data are normally distributed, and another test does not require any distributional assumptions for the data.
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Conjugate Gradient Acceleration of the EM Algorithm
TL;DR: The key, as it is shown, is that the EM step can be viewed as a generalized gradient, making it natural to apply generalized conjugate gradient methods in an attempt to accelerate the EM algorithm.
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Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data
Mortaza Jamshidian,Siavash Jalal +1 more
TL;DR: A modification of the proposed normal-theory Hawkins test for complete data is proposed to improve its performance, and its application to test of homoscedasticity and MCAR when data are multivariate normal and incomplete.