The newly developed stable maximum likelihood estimator (MLE) has been considered to be superior to the traditional robust estimation for Magnetotelluric (MT) transfer functions, based on the examination of field data. However, due to the unknown MT response function for real data, it is difficult...
One example of robust estimators for regression are adaptive modified maximum likelihood (AMML) estimators (Donmez, 2010). However, they are not robust to $x$ outliers, so-called leverage points. In this study, we propose a new regression estimator by employing an appropriate weighting scheme ...
Maximum Likelihood of One Parameter The R functionnlmminimizes arbitrary functions written in R. So to maximize the likelihood, we hand nlm the negative of the log likelihood. For the Cauchy location model (μis unknown, butσ=1is known) minus the log likelihood can be written either as > ...
In this paper, a novel approach towards horizon-based maximum likelihood (ML) state estimator is proposed that makes the state estimation process more robust against unmodeled and unstructured noise and disturbances in the state-space models. State space models provide a powerful way to perform state...
d evaluators Relaxes the requirement that the log-likelihood function be summable over the observations and thus suitable for all types of estimators. Robust estimates of variance, adjustment for clustering or survey design is not automatically done and dealing with this requires substantial effort ...
PropertiesoftheMaximumLikelihoodEstimator Wewillsketchformalproofsoftheseresults:Thelog-likelihoodfunction,againThelikelihoodequationandtheinformationmatrix.AlinearTaylorseriesapproximationtothefirstorderconditions:g(ML)=0g()+H()(ML-)(underregularity,higherordertermswillvanishinlargesamples.)Ourusualapproach.Large...
Although the shape of the log-likelihood is different from that of the likelihood, it is clear that both are maximized at approximately 1.7. We may solve analytically for the ML estimator. To maximize any function, we find the value of the parameters that make the first derivatives of the ...
The Super Robustness of Maximum Likelihood Location Estimator of Exponential Power Distribution, when p < 1 来自 Semantic Scholar 喜欢 0 阅读量: 8 作者: Q Gao 摘要: We proof that statistically, the maximum likelihood location estimator of exponential power distribution is strict super robust, when...
We derive uniform convergence rates for the maximum likelihood estimator and minimax lower bounds for parameter estimation in two-component location-scale Gaussian mixture models with unequal variances. We assume the mixing proportions o... M Linda 被引量: 0发表: 2016年 Maximum likelihood estimation ...
is the maximum likelihood estimate (mle) of θ. When expressed as a function of the random sample X, we have the maximum likelihood estimator (MLE) θˆ(X). Obviously, this method of finding estimators is in agreement with the likelihood principle, which says that the inferential conclusions...