Results 251 to 260 of about 831,124 (298)

The Open Syndrome Definition as a Machine-Readable Standard for Public Health: Design and Implementation Study. [PDF]

J Med Internet Res
Ferreira APG, Anžel A, Marcilio I, Hughes H, Elliot AJ, Kong JD, Schranz M, Ullrich A, Hattab G.   +8 more
europepmc   +1 more source

Updated International Patient Decision Aid Standards (IPDAS version 5.0): modified Delphi, evidence informed consensus process.

BMJ
Volk RJ, Lewis KB, Smith M, Carley M, Barry MJ, Bekker HL, Härter M, Hoffmann T, McCaffery K, Pignone M, Steffensen KD, Sepucha K, Thompson R, Trevena L, van der Weijden T, Witteman HO, Stacey D.   +16 more
europepmc   +1 more source

Marker Selection by Akaike Information Criterion and Bayesian Information Criterion

Genetic Epidemiology, 2001
We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected‐affected versus affected‐unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters.
Li, W., Nyholt, D.R.
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An Information Criterion for Informative Gene Selection

, 2005
It is important in bioinformatics research and applications to select or discover informative genes of a tumor from microarray data. However, most of the existing methods are based on models which assume that the gene expressions are normal distributed, which is often violated in practice.
Fei Ge, Jinwen Ma
openaire   +2 more sources

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An information criterion for likelihood selection

IEEE Transactions on Information Theory, 1999
Summary: For a given source distribution, we establish properties of the conditional density achieving the rate distortion function lower bound as the distortion parameter varies. In the limit as the distortion tolerated goes to zero, the conditional density achieving the rate distortion function lower bound becomes degenerate in the sense that the ...
A. Yuan, Bertrand S. Clarke
openaire   +1 more source

Subspace Information Criterion for Model Selection

Neural Computation, 2001
The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this article, we propose a new criterion for model selection, the subspace information criterion (SIC), which is a generalization of Mallows's CL.
Masashi Sugiyama, Hidemitsu Ogawa
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A Bayesian Information Criterion for Portfolio Selection

SSRN Electronic Journal, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Lan, Hansheng Wang 0002, Chih-Ling Tsai   +2 more
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Akaike's Information Criterion and the Histogram

Biometrika, 1987
By interpreting the histogram as a step-function, we explore the use of Akaike's information criterion in an automatic procedure to determine the histogram class width. We obtain an asymptotic relationship and present some results from a small simulation study.
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The Focused Information Criterion

Journal of the American Statistical Association, 2003
A variety of model selection criteria have been developed, of general and specific types. Most of these aim at selecting a single model with good overall properties, for example, formulated via average prediction quality or shortest estimated overall distance to the true model. The Akaike, the Bayesian, and the deviance information criteria, along with
Claeskens G., Hjort N.L.
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Generalized information criterion

Journal of Time Series Analysis, 2011
In this article, we propose a generalized Akaike's information criterion (AIC) (GAIC), which includes the usual AIC as a special case, for general class of stochastic models (i.e. i.i.d., non‐i.i.d., time series models etc.). Then we derive the asymptotic distribution of selected orderby GAIC, and show thatis inconsistent, i.e.(true order). This is the
Taniguchi, Masanobu, Hirukawa, Junichi
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