Results 251 to 260 of about 4,220,209 (306)

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
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On the uniqueness of the maximum likelihood estimator

Economics Letters, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Orme, Chris D., Ruud, Paul A.
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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
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Modified maximum likelihood estimator

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016
In this paper, we present a modified maximum likelihood estimation method, which is suitable to be used with φ-families rather than exponential families. An indicative result of the efficacy of this method is established. We perform numerical experiments to illustrate the accuracy of this method for estimating the dispersion parameter σ in φ-Gaussians.
David C. de Souza, Charles Casimiro Cavalcante, Rui Facundo Vigelis   +2 more
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The interpretation of maximum‐likelihood estimation

Canadian Journal of Statistics, 1984
AbstractMaximum‐likelihood estimation is interpreted as a procedure for generating approximate pivotal quantities, that is, functions u(X;θ) of the data X and parameter θ that have distributions not involving θ. Further, these pivotals should be efficient in the sense of reproducing approximately the likelihood function of θ based on X, and they should
Sprott, David A., Viveros-Aguilera, Román   +1 more
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Linear maximum likelihood estimator

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
A general linear and quasi-efficient estimator is presented which is an optimal (for a given criterion) approximation of the maximum likelihood estimator (MLE with nonlinear measurement equation) when the measurements are corrupted by a Gaussian noise. This approach consists of choosing a particular state vector which characterizes the signal.
Christian J. Musso, Claude Jauffret
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Moment Estimators and Maximum Likelihood

Biometrika, 1958
where J'q2(x) P(x; 0) dx = Or, J'q(x) qq(x) P(x; 0) dx = 0 (r+ s). (2) To avoid undue complication at this stage we assume P(x; 0) is continuous throughout its range. We reconsider the restrictions on P in a subsequent section.
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Maximum Likelihood Estimators on Manifolds

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
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A Semiparametric Maximum Likelihood Estimator

Econometrica, 1997
Summary: This paper presents a procedure for analyzing a model in which the parameter vector has two parts: a finite-dimensional component \(\theta\) and a nonparametric component \(\lambda\). The procedure does not require parametric modeling of \(\lambda\) but assumes that the true density of the data satisfies an index restriction.
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On maximum likelihood estimation of a Pareto mixture

Computational Statistics, 2011
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Bee, Marco, Benedetti, Roberto, Espa, Giuseppe   +2 more
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