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Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/22
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Limit theorems for bifurcating autoregressive processes with missing data
Probability (math.PR) Statistics Theory (math.ST)
2010/12/17
We study the asymptotic behavior of the least squares estimators of the unknown parameters of bifurcating autoregressive processes when some of the data are missing. We model the process of observed d...
Missing Data：A Comparison of Neural Network and Expectation Maximisation Techniques
Missing Data Neural Network Expectation Maximisation Techniques
2010/4/28
Two techniques have emerged from the recent literature as candidate solutions to the problem
of missing data imputation, and these are the Expectation Maximisation (EM) Algorithm and the
auto-associ...
Deviance Information Criteria for Missing Data Models
completion deviance DIC EM algorithm MAP model comparison mixture model random effect model
2009/9/21
The deviance information criterion(DIC)introduced by Spiegelhalter et al.
(2002) for model assessment and model comparison is directly inspired by linear
and generalised linear models, but it is ope...