Sentence examples for maximum likelihood imputation from inspiring English sources

Missing data were treated with maximum likelihood imputation.

x ^ t, u, k is the maximum likelihood imputation of the unreliable subsector x t, u, k for Gaussian k included in G s): x ^ t, u, k = arg max x t, u, k ≤ y t, u p ( x t, u, k | k ) k ∈ G ( s ).

Data will be analysed using an intention-to-treat approach with maximum likelihood imputation of missing values.

AMOS uses maximum likelihood imputation to estimate means and intercepts for missing data, which has been shown to have the least bias [ 26].

Contrast ii) Group B vs Group C. The primary analysis was by intention-to-treat [ 24] using maximum likelihood imputation of missing values (expectation maximisation: EM).

This criteria was used as Byrne [ 27] has shown that model and fit estimates are comparable between a complete data set and one with up to 25% data loss when a full information maximum likelihood imputation method is used.

The best method invokes a complex maximum likelihood statistical imputation method.

This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation.

A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation.

Estimating effects with maximum likelihood and multiple imputation converge to the same results in large sample sizes like ours [ 80].

Modern techniques for handling missing data (e.g. full information maximum likelihood and multiple imputation methods) are effective in adjusting for missingness that is dependent on variables with information from all participants [ 20].