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Abstract Details
Activity Number:
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141
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #305966 |
Title:
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MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random
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Author(s):
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Mortaza Jamshidian*+ and Siavash Jalal and Camden Jansen
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Companies:
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California State University Fullerton and University of California at Los Angeles and University of California at Irvine
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Address:
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Department of Mathematics, Fullerton, CA, 92834, United States
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Keywords:
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missing data ;
imputation ;
Test of MCAR ;
Maximum Likelihood ;
Homogeneity of covariances ;
Hawkins test
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Abstract:
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Researchers are often faced with analyzing data sets that are not complete.Such data analysis requires the knowledge of the missing data mechanism. If data are missing completely at random (MCAR), many missing data analysis techniques leas to valid inference, and thus, it is desirable to test for MCAR. The package MissMech implements two tests for this purpose that were developed by Jamshidian and Jalal (2010). These tests can be run using a function called TestMCARNormality. One of the tests is valid if data are normally distributed, and the other test does not assume any distributional assumptions for the data. In addition to testing MCAR, in some special cases the function TestMCARNormality is also able to test whether data have a multivariate normal distribution. As a bonus, the functions in MissMech can also be used to perform several additional tasks including (1) Test of homoscedasticity for several groups when data are completely observed, (2) impute incomplete data sets assuming normality or non-normality, (3) Obtain ML estimated of mean and covariance for incomplete data,(4) perform $k$-sample test, and (5) perform Neyman test of goodness of fit.
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