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Modeling the Role of Baseline Risk and Additional Study-Level Covariates in Meta-Analysis of Treatment Effects. [PDF]

Stat Med
Tran PT, Guolo A.
europepmc   +1 more source

Revealing the range of equally likely estimates in the admixture model. [PDF]

G3 (Bethesda)
Heinzel CS, Baumdicker F, Pfaffelhuber P.   +2 more
europepmc   +1 more source

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Maximum Likelihood Estimators on Manifolds

International Conference on Geometric Science of Information, 2017
Maximum likelihood estimator (MLE) is a well known estimator in statistics. The popularity of this estimator stems from its asymptotic and universal properties. While asymptotic properties of MLEs on Euclidean spaces attracted a lot of interest, their studies on manifolds are still insufficient.
Hatem Hajri, Salem Said, Yannick Berthoumieu   +2 more
openaire   +2 more sources

Improved maximum-likelihood estimators for the parameters of the unit-gamma distribution

Communications in Statistics - Theory and Methods, 2018
Josmar Mazucheli, André F B Menezes, Sanku Dey   +2 more
exaly   +2 more sources

Bias-corrected maximum likelihood estimators of the parameters of the inverse Weibull distribution

Communications in Statistics Part B: Simulation and Computation, 2019
Josmar Mazucheli, André F B Menezes, Sanku Dey   +2 more
exaly   +2 more sources

A comparison of full information maximum likelihood and multiple imputation in structural equation modeling with missing data.

Psychological methods, 2021
This article compares two missing data procedures, full information maximum likelihood (FIML) and multiple imputation (MI), to investigate their relative performances in relation to the results from analyses of the original complete data or the ...
Taehun Lee, Dexin Shi
semanticscholar   +1 more source

Robust Maximum Likelihood Estimation

INFORMS Journal on Computing, 2019
In many applications, statistical estimators serve to derive conclusions from data, for example, in finance, medical decision making, and clinical trials. However, the conclusions are typically dependent on uncertainties in the data. We use robust optimization principles to provide robust maximum likelihood estimators that are protected against data ...
Dimitris Bertsimas, Omid Nohadani
openaire   +2 more sources

On bias in maximum likelihood estimators

Journal of Statistical Planning and Inference, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mardia, K. V., Southworth, H. R., Taylor, C. C.   +2 more
openaire   +2 more sources