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Topic: Akaike information criterion

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	APPENDIX 1: Making sense out of Akaike’s Information Criterion (AIC): its use and interpretation in model ...
		A few decades later, measures of information, such as the Akaike information criteria (AIC) and associated measures of model uncertainty, have begun to surface in the ecological disciplines.
		AIC VS HO The AIC is not a hypothesis test, does not have an α -value and does not use notions of significance.
		The AIC provides an objective way of determining which model among a set of models is most parsimonious, as we do not rely on α.
	www.theses.ulaval.ca /2004/21842/apa.html (3835 words)

	Amazon.de: Model Selection and Inference. A Practical Information-Theoretic Approach: English Books: Kenneth P. ... (Site not responding. Last check: 2007-11-06)
		This leads to Akaike's Information Criterion (AIC) and various extensions and these are relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case.
		The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems.
		The value of AIC is computed for each a priori model to be considered and the model with the minimum AIC is used for statistical inference.
	www.amazon.de /Selection-Inference-Practical-Information-Theoretic-Approach/dp/0387985042 (630 words)

	INAMORI FOUNDATION (via CobWeb/3.1 planetlab2.isi.jhu.edu) (Site not responding. Last check: 2007-11-06)
		This criterion established a new paradigm that bridged the world of data and the world of modeling, thus contributing greatly to the information and statistical sciences.
		Consequently, the role and meaning of the AIC as a criterion for estimating statistical models have become extremely significant in the development of statistical science.
		The AIC is built into commercial statistical software packages, and is also widely used in such diverse areas as gene analysis; image compression technologies; and vehicle stability control technologies, among many others.
	www.inamori-f.or.jp.cob-web.org:8888 /laureates/k22_b_hirotugu/prs_e.html (622 words)

	Model Selection (Site not responding. Last check: 2007-11-06)
		Unifying the derivations of the Akaike and corrected Akaike information criteria.
		An Akaike information criterion for model selection in the presence of incomplete data (with R. Shumway).
		Generalizing the derivation of the Schwarz information criterion (with A. Neath).
	myweb.uiowa.edu /cavaaugh/mspub.html (254 words)

	Akaike information criterion - Wikipedia, the free encyclopedia
		The Akaike information criterion (AIC) (pronounced ah-kah-ee-keh), developed by Hirotsugu Akaike in 1971 and proposed in Akaike (1974), is a measure of the goodness of fit of an estimated statistical model.
		Hence AIC not only rewards goodness of fit, but also includes a penalty that is an increasing function of the number of estimated parameters.
		in terms of "information," combining not only a transformation of the logarithm of the likelihood of the model but also, following Akaike (1974), a penalty that is an increasing function of the number of estimated parameters.
	en.wikipedia.org /wiki/Akaike_information_criterion (465 words)

	US/Japan Proc.Vol_1
		On May 24 to 29, 1992, The US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, was held at the Department of Statistics, University of Tennessee, Knoxville, to commemorate the sixty-fifth birthday of Professor Hirotugu Akaike and to honor him for his revolutionary contributions to modern statistical theory and practice.
		AIC is clearly one of the most interesting and important developments in the field of statistics in recent years.
		By combining ideas related to what is now called "predictive efficiency" with the notion of Kullback-Leibler information, Akaike arrived at AIC for evaluating alternative statistical models which cleverly combines a measure of goodness-of-fit of the model with a term relating to the number of parameters used to achieve that fit.
	web.utk.edu /~bozdogan/volume1.html (2171 words)

	[No title] (Site not responding. Last check: 2007-11-06)
		Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaike's Information Criterion (AIC).
		This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value.
		The form of AIC is derived in both the multivariate analysis of variance (MANOVA) model and in the multivariate model with varying mean vectors and variance-covariance matrices.
	www.uic.edu /classes/idsc/ids594/my_abstracts/abstr25.html (112 words)

	Annotated Bibliography, Model Selection (Site not responding. Last check: 2007-11-06)
		Akaike, H. Information theory and an extension of the maximum likelihood principle.
		Bozdogan, H. Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions.
		He offers some insight into the AIC vs. BIC question, although he seems to back-peddle on the "true model" issue (e.g., "true" doesn't really need to mean true).
	www.ori.org /~keiths/bibliography/statistics-selection.html (703 words)

	Deviance information criterion - Wikipedia, the free encyclopedia
		The deviance information criterion (DIC) is a hierarchical modeling generalization of the AIC (Akaike information criterion) and BIC (Bayesian information criterion, also known as the Schwarz criterion).
		It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation.
		Like AIC and BIC it is an asymptotic approximation as the sample size becomes large.
	en.wikipedia.org /wiki/Deviance_information_criterion (381 words)

	Methods for Model Selection Conference - Announcement
		During the last twenty five years, Akaike's (1973) entropic information criterion, which is known as AIC, has had a fundamental impact in statistical model evaluation problems.
		Analytic formulation of ICOMP takes the "spirit" of Akaike's (1973) AIC, but it is based on the generalization and utilization of an entropic covariance complexity (COVCOMP) index of van Emden (1971) of a multivariate normal distribution in parametric estimation.
		The criterion for selection is the degree to which a model based on the available sample will reflect the behavior of future observations.
	quantrm2.psy.ohio-state.edu /injae/modselx.htm (4056 words)

	Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions. (Site not responding. Last check: 2007-11-06)
		Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions.
		This paper studies the general theory of Akaike's Information Criterion (AIC) and provides two analytical extensions.
		The extensions make AIC asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the two models.
	www.eric.ed.gov /sitemap/html_0900000b8005edf2.html (91 words)

	Springer Science+Business Media : Press Releases (via CobWeb/3.1 planetlab2.isi.jhu.edu) (Site not responding. Last check: 2007-11-06)
		Akaike will receive the award for his contributions to statistical science and modeling by developing an information criterion known today as the Akaike Information Criterion (AIC).
		He formulated the AIC to facilitate selection of the most appropriate model from a number of different types of models.
		Hirotugu Akaike was born in Fujinomiya, Shizuoka, Japan in 1927.
	www.springer-sbm.com.cob-web.org:8888 /index.php?id=291&backPID=132&L=0&tx_tnc_news=2573 (374 words)

	Akaike's Information Criterion (via CobWeb/3.1 planetlab2.isi.jhu.edu) (Site not responding. Last check: 2007-11-06)
		"Akaike's Information Criterion is a criterion for selecting among nested econometric models."
		"Akaike (1973) defined the most well-known criterion as AIC = - ln L + p, where L is the likelihood for an estimated model with p parameters."
		Unifying the Derivations for the Afaike and Corrected Akaike Information Criterion
	www.modelselection.org.cob-web.org:8888 /aic (277 words)

	NPWRC :: Alien Plant Invasion in Mixed-grass Prairie: Effects of Vegetation Type and Anthropogenic Disturbance
		Akaike's Information Criterion (AIC) indicated that the fully parameterized model, including the interaction among vegetation type, disturbance, and park unit, best described the distribution of both total number of alien plants per transect and frequency of alien plants on transects where they occurred.
		Although all vegetation types were invaded by alien plants, mesic communities had both greater numbers and higher frequencies of alien plants than did drier communities.
		Figure 1 -- Akaike's Information Criterion identified the fully parameterized model, including the vegetation type × park unit × disturbance interaction, as the best model for both (a) mean number of alien species on transects and (b) mean frequency of alien plants on occupied transects.
	www.npwrc.usgs.gov /resource/plants/apinvas (716 words)

	Preliminary Autoregressive Models
		More information on the Yule-Walker equations in the multivariate setting can be found in Whittle (1963) and Ansley and Newbold (1979).
		The Akaike information criterion, or AIC, is defined as -2(maximum of log likelihood)+2(number of parameters).
		Although the autoregressive models can be used for prediction, their primary value is to aid in the selection of a suitable portion of the sample covariance matrix for use in computing canonical correlations.
	www.asu.edu /it/fyi/dst/helpdocs/statistics/sas/sasdoc/sashtml/ets/chap18/sect17.htm (717 words)

	[No title]
		Akaike suggested maximizing the criterion to choose between models with different numbers of parameters.
		Akaike Information Criterion (AIC) can be used in Generalized Linear/Nonlinear Models (GLZ) when comparing the subsets of effects during best subset regression.
		As opposed to heuristics (which contain general recommendations based on statistical evidence or theoretical reasoning), algorithms are completely defined, finite sets of steps, operations, or procedures that will produce a particular outcome.
	www.statsoft.com /textbook/glosa.html (2294 words)

	13. Detecting Major Genes
		Background: Page 363 discusses Akaike's information content, a measure to compare the fit of different models by adjusting the likelihood for the number of parameters fit.
		IC used as a model selection criterion is large sample based.
		Bozdogan H (1987) Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions.
	nitro.biosci.arizona.edu /zbook/volume_1/chapters/vol1_13.html (740 words)

	Detection of genes with tissue-specific expression patterns using Akaike's information criterion procedure -- Kadota et ...
		AIC procedure is specifically applicable to the extraction of
		Akaike H. A Baysian analysis of the minimum AIC procedure.
		Kitagawa G. On the use of AIC for the detection of outliers.
	physiolgenomics.physiology.org /cgi/content/full/12/3/251 (3707 words)

	AMCA: Akaike's information criterion for change-point model by Yoshiyuki Ninomiya (Site not responding. Last check: 2007-11-06)
		AIC-type information criterion is generally estimated by the bias-corrected maximum log-likelihood.
		This presentation considers the AIC-type information criterion for change-point models, which are not regular, the bias of which will not be the same as for regular models.
		The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Mathematical Conference Abstracts.
	at.yorku.ca /c/a/r/m/51.htm (158 words)

	Energy Citations Database (ECD) - Energy and Energy-Related Bibliographic Citations (Site not responding. Last check: 2007-11-06)
		Availability information may be found in the Availability, Publisher, Research Organization, Resource Relation and/or Author (affiliation information) fields and/or via the "Full-text Availability" link.
		Excess errors in the estimation procedures are eliminated by introducing an estimation strategy utilizing Akaike's information criterion.
		We make a quantitative comparison between the errors of the experimentally estimated states and their asymptotic lower bounds, which are derived from the Cramer-Rao inequality.
	www.osti.gov /energycitations/product.biblio.jsp?osti_id=20640030 (250 words)

	Akaike information criterion
		AIC = OFV + 2p, where p is total number of parameters.
		AIC has an asymptotic probability of one of choosing a good subset
		Comparison of the Akaike Information Criterion, the Schwarz Criterion and
	www.cognigencorp.com /nonmem/nm/99jul12001.html (309 words)

	A Note On the Unification of the Akaike Information Criterion - Shi, Tsai (ResearchIndex) (Site not responding. Last check: 2007-11-06)
		Abstract: this paper is first to propose a generalized Kullback-Leibler information that can measure the discrepancy between a robust function evaluated under both the true model and fitted models.
		Shi P D, Tsai C L. A note on the unification of the Akaike Information Criterion.
		@misc{ shi98note, author = "P. Shi and C. Tsai", title = "A note on the unification of the Akaike Information Criterion", text = "Shi P D, Tsai C L. A note on the unification of the Akaike Information Criterion.
	citeseer.ist.psu.edu /56332.html (491 words)

	Model Fitting Information (Site not responding. Last check: 2007-11-06)
		The -2 Log Likelihood statistic has a chi-square distribution under the null hypothesis (that all the explanatory variables in the model are zero) and the procedure produces a p-value for this statistic.
		The AIC and SC statistics give two different ways of adjusting the -2 Log Likelihood statistic for the number of terms in the model and the number of observations used.
		These statistics should be used when comparing different models for the same data (for example, when you use the METHOD=STEPWISE option in the MODEL statement); lower values of the statistic indicate a more desirable model.
	www.okstate.edu /sas/v7/sashtml/books/stat/chap35/sect21.htm (176 words)

	Pi2 onset time determination with information criterion
		Second the proposed method determines the Pi2 onset so that the intervals before and after the onset can be optimally described by different time series models.
		The optimal partition is determined by minimizing the Akaike information criterion (AIC).
		The time series models adopted are general enough to apply to any nonstationary time series irrespective of its waveform, frequency, amplitude, and signal-to-noise ratio.
	www.agu.org /pubs/crossref/2002/2001JA003505.shtml (334 words)

	[No title] (Site not responding. Last check: 2007-11-06)
		A ruler to measure the similarity between the statistical model and the true distribution is the Kullback-Leibler information number It can be shown that -I(g,f) is the entropy.
		The reason for such bias to occur is that the same data are used to estimate parameters and to calculate the log-likelihood.
		An unbiased estimate of yields the famous Akaike Information Criterion (AIC).
	www.umiacs.umd.edu /~shaohua/enee698a_f03/aic.ppt (1103 words)

	A Method to Determine the Structure of an Unknown Mixture Using the Akaike Information Criterion and the Bootstrap - ... (Site not responding. Last check: 2007-11-06)
		A Method to Determine the Structure of an Unknown Mixture Using the Akaike Information Criterion and the Bootstrap
		Although the problem of determining the number of terms is intractable under the most general assumptions there is hope of elucidating the mixture structure given appropriate caveats on the underlying mixture.
		This paper examines a new approach to this problem based on the use of Akaike Information Criterion (AIC) based pruning of data driven mixture models which are obtained from resampled data sets.
	www.stormingmedia.us.cob-web.org:8888 /59/5928/A592882.html (184 words)

	AIC (via CobWeb/3.1 planetlab2.isi.jhu.edu) (Site not responding. Last check: 2007-11-06)
		The Akaike Information Criterion determines the model order p by minimizing an information theoretic function of p, AIC(p).
		The term 2p is a "penalty" for the use of extra AR coefficients that do not substantially reduce the prediction error.
		The "AIC minimum" is only one of many criteria proposed for the selection of the AR order.
	www.cbi.dongnocchi.it.cob-web.org:8888 /glossary/AIC.html (138 words)

	Small mammal communities of streamside management zones in intensively managed pine forests of Arkansas (Site not responding. Last check: 2007-11-06)
		Streamside management zones in the South designed to meet voluntary water quality standards are likely sufficient for small mammal conservation.
		Akaike's information criterion, arkansas, abundance, diversity, intensive forestry, pine plantations, riparion zones, small mammals, streamside management zones
		This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
	www.srs.fs.usda.gov /pubs/21070 (649 words)

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