Results 251 to 260 of about 2,646,945 (311)

PAGE: an R package for network detection of multivariate error-prone gene expression data with the availability of auxiliary information. [PDF]

BMC Bioinformatics
Chen LP, Chang WY.
europepmc   +1 more source

A New Estimator of Kullback-Leibler Divergence via Shannon Entropy. [PDF]

Entropy (Basel)
Çadırcı MS, Singull M.
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Scalable estimation of temporal clustering in accelerometry: a kernel-independent dispersion index grounded in the Hawkes process

Zheng X, Danilevicz IM, Paw M.
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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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