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Likelihood Function
"... Wilcox, Gebbie & Mbambiso Spin, stochastic factor models, and a genetic algorithm A non-parametric clustering technique ..."
Abstract
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Wilcox, Gebbie & Mbambiso Spin, stochastic factor models, and a genetic algorithm A non-parametric clustering technique
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
, 2001
"... Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
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Cited by 948 (62 self)
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penalized likelihood functions. The proposed ideas are widely applicable. They are readily applied to a variety of parametric models such as generalized linear models and robust regression models. They can also be applied easily to nonparametric modeling by using wavelets and splines. Rates of convergence
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
- ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
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Cited by 1051 (0 self)
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The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score
Non-Gaussian Likelihood Function
, 1995
"... We generalize the maximum likelihood method to non-Gaussian distribution functions by means of the multivariate Edgeworth expansion. We stress the potential interest of this technique in all those cosmological problems in which the determination of a non-Gaussian signature is relevant, e.g. in the a ..."
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We generalize the maximum likelihood method to non-Gaussian distribution functions by means of the multivariate Edgeworth expansion. We stress the potential interest of this technique in all those cosmological problems in which the determination of a non-Gaussian signature is relevant, e
Accelerating the Phylogenetic Likelihood Function
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Likelihood Functions for Stochastic Signals in
, 1969
"... For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym derivative is given. These results were stated in a previous paper (Duncan 1968). Independent of the absolute continuity result, a modification is proved for the hypothesis with signal present. 1. ..."
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For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym derivative is given. These results were stated in a previous paper (Duncan 1968). Independent of the absolute continuity result, a modification is proved for the hypothesis with signal present. 1.
Likelihood Function Experiments
"... Probabilistic graphical models •Represent complex dependencies between ..."
APPROXIMATING THE LIKELIHOOD FUNCTION OF CMB EXPERIMENTS
, 1998
"... ABSTRACT We discuss the problem of constraining cosmological parameters with cosmic microwave background band–power estimates. Because these latter are variances, they do not have gaussian distribution functions and, hence, the standard χ 2 –approach is not strictly applicable. A general purpose app ..."
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approximation to experimental band–power likelihood functions is proposed, which requires only limited experimental details. Comparison with the full likelihood function calculated for several experiments shows that the approximation works well. KEYWORDS: 1.
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