Good day, I am interested in understanding a research paper that used the maximum likelihood of a generalized linear mixed model to predict the missing data from participants who did not complete an intervention in a randomized control trial.
I am not very familiar with statistical functions, and would like to understand this concept in simple terms to explain to my peers. Any information is appreciated.
Hello Edbert,
The maximum likelihood method does not impute any data, but rather uses each case's available data to compute maximum likelihood estimates. The maximum likelihood estimate is the value of the parameter that is most likely to have occurred in the observed data. With missing data, the likelihood is computed separately for those cases with complete data on some variables and those with complete data on all variables. Then these two likelihoods are maximized together to find the estimates. This method gives unbiased parameter estimates and standard errors, but it is, however, limited to linear models.
This paper is easy to follow and can help you understand further:
https://web.njit.edu/~wguo/Math654_2012/YatFan_Victor.pdf
Best wishes!
You know Gleneralized linear model is an umberella terms which has many methods.Here parameter estimation is done using maximum likelihood method(Betas).In linear regression those estimations are done using commonly using least square method.
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