Results 211 to 220 of about 288,039 (268)

Finite Mixture Modelling of Random-effects Distributions in Meta-analysis: A Simulation Study with an Application

Babalola I, Manda S, Maharela I.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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

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
openaire   +1 more source

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
openaire   +1 more source

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.
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +1 more source

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.
openaire   +1 more source

A Note on a Maximum-Likelihood Estimate

Econometrica, 1947
An estimate of y obtained by applying the method of maximum likelihood under the assumption that ut is normally distributed is consistent and asymptotically normally distributed. The asymptotic standard deviation is given in this note. Although Kendall considers many estimates of the period in his publication, he does not use the maximum-likelihood ...
openaire   +2 more sources