Bayesian regression methods intended as robust to outliers include ε−contamination priors (Moreno and Pericchi, 1993), modified likelihoods such as weighted likelihoods (Greco et al. 2008; Agostinelli and Greco, 2013), and localized regression (Wang and Blei, 2017).
If the genetic variants are in linkage disequilibrium and the correlations between the variants are known, then these correlations can be used in a modified likelihood-based model: The correlations between the genetic variants are the same as the correlations between the beta coefficients corresponding to the genetic variants.
He concludes that the maximum likelihood, modified maximum likelihood and moment methods show better performance in simulation tests.
The Weibull shape and scale parameters are calculated using different methods like empirical, maximum likelihood, modified maximum likelihood, graphical, and energy pattern.
The methods used including empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), and energy pattern (EPEM) were fitted very well except the graphical method (GM).
The Weibull k and c parameters are estimated using five methods including empirical, maximum likelihood, graphical, modified maximum likelihood, and energy pattern for wind speed data measured at two heights 30 and 50 m.
The Weibull parameters have been calculated using empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), energy pattern (EPFM), and graphical (GM) methods to determine the other dependent parameters.
The investigated methods are the mean wind speed method, the maximum likelihood method, the modified maximum likelihood method, the graphical method and the power density method.
Three methods for calculating the parameters of the Weibull wind speed distribution for wind energy analysis are presented: the maximum likelihood method, the proposed modified maximum likelihood method, and the commonly used graphical method.
A modified generalized likelihood ratio test (mGLRT) proposed here uses the following likelihood ratio for decision: (4).
Meanwhile, some modified maximum likelihood type estimators were developed, such as the local maximum likelihood type estimator (local M-estimators), which is a combination of the local linear regression and the M-estimation regression, so the nice properties of local linear estimator and M-estimator persist.
