Why maximum likelihood is better than multiple imputation

Maximum Likelihood is Better than Multiple Imputation: Part II

Maximum Likelihood is Better than Multiple Imputation: Part II · ML is simpler to implement (if you have the right software). · Unlike multiple ...

When multiple imputation is better than maximum likelihood

Multiple imputation is much more flexible, and can be applied in more situations than maximum likelihood.

Why Maximum Likelihood is Better Than Multiple Imputation

I prefer to use maximum likelihood to handle missing data whenever possible. One reason is that ML is simpler, at least if you have the right software.

Missing data and maximum likelihood - Cross Validated

But if you have the full/correct likelihood, then yes the maximum likelihood ... likelihood is a good (probably better) alternative to imputation.

A comparison of full information maximum likelihood and ... - PubMed

This article compares two missing data procedures, full information maximum likelihood (FIML) and multiple imputation (MI), to investigate their relative ...

Maximum Likelihood vs. Multiple Imputation (combined ... - Statalist

I am considering, if I should use: - Multiple Imputation, or - Maximum Likelihood At the end I would like to perform a hierarchical regression (command: "hireg ...

Maximum Likelihood Versus Multiple Imputation for Missing Data in ...

In conclusion, ML appears to be preferable to MI in research conditions with small missing samples and multivariate nonnormality whether or not strong prior ...

Missing Data Analysis: Multiple Imputation and Maximum Likelihood ...

Missing Data Analysis: Multiple Imputation and Maximum Likelihood Methods. 9.9K views · 6 years ago ...more ...

When and how should multiple imputation be used for handling ...

Unlike multiple imputation, full information maximum likelihood has no potential problems with incompatibility between the imputation model and ...

Maximum likelihood versus multiple imputation for missing data in ...

The study examined the performance of maximum likelihood (ML) and multiple imputation (MI) procedures for missing data in longitudinal research when fitting ...

Mplus Discussion >> FIML vs MI

All maximum likelihood estimators use what people refer to as FIML for missing data. FIML and multiple imputation are asymptotically similar ...

Missing Data Part II: Multiple Imputation & Maximum Likelihood

Appendix B shows how to do multiple imputation when more than one variable has missing data. Appendix C shows roughly how multiple imputation ...

Maximum likelihood, multiple imputation and regression calibration ...

Our results indicate that with large measurement error or large enough sample sizes, ML performs as well or better than MI and RC. However, for smaller ...

Missing Data Imputation versus Full Information Maximum ...

The data was generated with varying levels of missing data, dependencies at the second level, and different sample sizes at both the first and ...

A comparison of multiple imputation strategies to deal with missing ...

Specifically, FIML produced parameter estimates by iteratively maximizing the sum of N case-wise log-likelihood functions tailored to individual ...

Handling Missing Data by Maximum Likelihood - Semantic Scholar

It is argued that maximum likelihood is usually better than multiple imputation for several important reasons, and it is demonstrated how maximum likelihood ...

The second method is to analyze the full, incomplete data set using maximum likelihood estimation. This method does not impute any data, but rather uses each ...

Maximum likelihood multiple imputation - arXiv

Under MLMI, BMI needs more imputation than the WB estimator if the fraction of missing information is less than .6, but BMI needs fewer imputations than the ...

Maximum likelihood versus multiple imputation for missing data in ...

In conclusion, ML appears to be preferable to MI in research conditions with small missing samples and multivariate nonnormality whether or not ...

Maximum Likelihood Versus Multiple Imputation for Missing Data in ...

For this, full information maximum likelihood estimation (FIML) was used to estimate SEM parameters. FIML is a more appropriate method for addressing missing ...