Latent class methods for data analysis with randomly missing covariates

Host Institution:

La Trobe University

Title of Seminar:

Latent class methods for data analysis with randomly missing covariates

Speaker's Name:

Professor Murray Aitkin

Speaker's Institution:

Department of Mathematics and Statistics, University of Melbourne

Time and Date:

Friday 22 March, 12.30 PM (EDT)

Seminar Abstract:

Randomly missing covariates are endemic in survey analysis in all fields. Multiple imputation (MI) methods for their accommodation are now in widespread use, assisted by many statistical package procedures. An important requirement for MI is the imputation model - the model for the conditional distribution of the incomplete covariates given the complete covariates. Practical computational costs usually lead to the use of the multivariate normal model for this purpose, even when covariates are binary, discrete or continuous but non-normal. This talk develops "nonparametric" analysis methods based on the latent class model for the incomplete covariates, and gives several small examples. 

Seminar Convenor:

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AGR IT support:

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