robust maximum likelihood model
Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... This paper is organized as follows. In Section 2 we discuss how to take account of the two constraints: symmetry of the variance matrices and the fact that the weights sum to one. Our general result (Theorem 1) is formu- ...
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On robust estimation for slope in linear functional relationship model
... the robust estimator and robust coefficient correlation in estimating the slope ...the maximum likelihood estimation including the nonparametric method (Ghapor et ...modified maximum ...
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Readings in Targeted Maximum Likelihood Estimation
... double robust property of the targeted maximum likelihood estimator obviates the need for accurate estimation of both Q and g since correct specification of either one leads to consistent estimates ...
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Smoothing Algorithms by Constrained Maximum Likelihood
... constrained maximum likelihood ...constrained maximum likelihood, with a fair risk scale determined by constrained maximum likelihood, leading to a fair and more justified loss ...
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The Comparison Between Maximum Weighted and Trimmed Likelihood Estimator of The Simple Circular Regression Model
... a robust method to estimate the unknown parameters in the linear regression ...extend maximum weighted likelihood estimator to estimate the parameters of the simple circular regression ...regression ...
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Robust estimation for ordinal regression.
... this model is Maximum Likelihood ...the Maximum Likelihood estimator, yielding an estimator with bounded influence ...Weighted Maximum Likelihood estimator allows to ...
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Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering
... Despite our effort to make the simulation study fair, ultimately it would be good to have comparisons of methods run by researchers who did not have their hand in the design of any of the methods. Every method was best ...
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Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
... of model misspecification are important for several reasons. First, model misspecification may be present in many, if not most, situations; and so robust methods that address the assumption of ...
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Targeted Maximum Likelihood Based Causal Inference
... targeted maximum likelihood step removes bias for the target parameter if the censoring/treatment mechanism used in the targeted MLE step is estimated ...targeted maximum likelihood step the ...
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The breakdown behavior of the maximum likelihood estimator in the logistic regression model.
... in robust statistics is to build high breakdown point ...many robust estimators have been calculated. Robust estimators have also been introduced for the logistic regression model, but their ...
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Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering
... mixture model by adding an improper constant mixture component to catch outliers and points that cannot appropriately assigned to any ...other robust clustering methods aiming for approximately Gaussian ...
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Distributions of Maximum Likelihood Estimators and Model Comparisons
... the model that is assumed for ...estimation model is one of a number of possible candidate models for a ...generating model, including distrib- ution free and robust approaches to ...
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Robust maximum likelihood training of heteroscedastic probabilistic neural networks
... parsimonious model for PDF ...a robust ML learning method by incorporating a robust statistical tech- nique known as the Jack-knife with the EM ...
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Efficient estimation of Pareto model using modified maximum likelihood estimators
... Tanvir Ahmad received his PhD in Statistics from Islamia University Bahawalpur, Pakistan, He received his MSc from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. He was with University of ...
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GAML: genome assembly by maximum likelihood
... The model described above does not penalize obvious misassemblies when two contigs are joined together without any evidence in the ...the likelihood function applicable as an optimi- zation criterion for ...
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Maximum likelihood estimation of variance components
... components model can be used to describe data arising in fields as diverse as designed experiments in agriculture and observational studies in the social ...components model, in particular, which estimation ...
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Maximum likelihood representation of MIPAS profiles
... Eyring, V., Butchart, N., Waugh, D. W., Akiyoshi, H., Austin, J., Bekki, S., Bodeker, G. E., Boville, B. A., Brühl, C., Chipper- field, M. P., Cordero, E., Dameris, M., Deushi, M., Fioletov, V. E., Frith, S. M., Garcia, ...
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Estimation and tests for power-transformed and threshold GARCH models
... ARCH model (Higgns and Bera (1992)) when δ = 2, q = 0, a TARCH model (Li and Li (1996)) when δ = 1/2, q = 0, a power-transformed and threshold ARCH model (Hwang and Kim (2004)) when q = 0, a Box-Cox ...
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Robust Element Wise Empirical Likelihood Estimation Method for Longitudinal Data
... empirical likelihood methods are al- so one of the frequently used ...empirical likelihood (EL) method was originally applied by Owen (1988) [11] to the estimation of the population mean of completely ...
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Investigating the psychosocial mechanisms underlying the influence of trait emotional intelligence on university adjustment: the mediating roles of coping and social support
... Heretofore, no studies have investigated the psychosocial mechanisms underlying the effects of trait emotional intelligence (trait EI) on recent ƐĐŚŽŽůůĞĂǀĞƌƐ͛ĂĚũƵƐƚŵĞŶƚƚŽƵŶŝǀĞƌƐŝƚLJ͘dŚŝƐŝƐƐƵƌƉƌŝƐŝŶŐƐŝŶĐĞƚƌĂŝƚ/ŝƐ͕ ...
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