Deviance Information Criterion
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Deviance Information Criterion
The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). 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. DIC is an asymptotic approximation as the sample size becomes large, like AIC. It is only valid when the posterior distribution is approximately multivariate normal. Definition Define the deviance as D(\theta)=-2 \log(p(y, \theta))+C\, , where y are the data, \theta are the unknown parameters of the model and p(y, \theta) is the likelihood function. C is a constant that cancels out in all calculations that compare different models, and which therefore does not need to be known. There are two calculations in common usage for the effective number of parameters of the model. The first, as described in , is p_D=\overline-D(\bar), where \bar is the expectation of \theta. The second, as ...
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Hierarchical Linear Model
Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available. Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level ...
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Bayesian Statistics
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a ''degree of belief'' in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after many trials. Bayesian statistical methods use Bayes' theorem to compute and update probabilities after obtaining new data. Bayes' theorem describes the conditional probability of an event based on data as well as prior information or beliefs about the event or conditions related to the event. For example, in Bayesian inference, Bayes' theorem can be used to estimate the parameters of a probability distribution or statistical model. Since Bayesian statistics treats probabi ...
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YouTube
YouTube is a global online video platform, online video sharing and social media, social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the List of most visited websites, second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. , videos were being uploaded at a rate of more than 500 hours of content per minute. In October 2006, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone, to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube also approved creators to participate in Google's Google AdSens ...
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Journal Of The Royal Statistical Society, Series B
The ''Journal of the Royal Statistical Society'' is a peer-reviewed scientific journal of statistics. It comprises three series and is published by Wiley for the Royal Statistical Society. History The Statistical Society of London was founded in 1834, but would not begin producing a journal for four years. From 1834 to 1837, members of the society would read the results of their studies to the other members, and some details were recorded in the proceedings. The first study reported to the society in 1834 was a simple survey of the occupations of people in Manchester, England. Conducted by going door-to-door and inquiring, the study revealed that the most common profession was mill-hands, followed closely by weavers. When founded, the membership of the Statistical Society of London overlapped almost completely with the statistical section of the British Association for the Advancement of Science. In 1837 a volume of ''Transactions of the Statistical Society of London'' were wri ...
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CRC Press
The CRC Press, LLC is an American publishing group that specializes in producing technical books. Many of their books relate to engineering, science and mathematics. Their scope also includes books on business, forensics and information technology. CRC Press is now a division of Taylor & Francis, itself a subsidiary of Informa. History The CRC Press was founded as the Chemical Rubber Company (CRC) in 1903 by brothers Arthur, Leo and Emanuel Friedman in Cleveland, Ohio, based on an earlier enterprise by Arthur, who had begun selling rubber laboratory aprons in 1900. The company gradually expanded to include sales of laboratory equipment to chemists. In 1913 the CRC offered a short (116-page) manual called the ''Rubber Handbook'' as an incentive for any purchase of a dozen aprons. Since then the ''Rubber Handbook'' has evolved into the CRC's flagship book, the '' CRC Handbook of Chemistry and Physics''. In 1964, Chemical Rubber decided to focus on its publishing ventures ...
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Nils Lid Hjort
Nils Lid Hjort (born 12 January 1953) is a Norwegian statistician, who has been a professor of mathematical statistics at the University of Oslo since 1991. Hjort's research themes are varied, with particularly noteworthy contributions in the fields of Bayesian probability (Beta processes for use in non- and semi-parametric models, particularly within survival analysis and event history analysis, but also with links to Indian buffet processes in machine learning), density estimation and nonparametric regression (local likelihood methodology), model selection ( focused information criteria and model averaging), confidence distributions, and change detection. He has also worked with spatial statistics, statistics of remote sensing, pattern recognition, etc. An article on frequentist model averaging, with co-author Gerda Claeskens, was selected as ''Fast Breaking Paper in the field of mathematics'' by the Essential Science Indicators in 2005. This and a companion paper, both publis ...
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Gerda Claeskens
Gerda Claeskens is a Belgian statistician. She is a professor of statistics in the Faculty of Economics and Business at KU Leuven, associated with the KU Research Centre for Operations Research and Business Statistics (ORSTAT). Contributions Claeskens is an expert in nonparametric statistics and in model selection, including model averaging. She is known for developing, with Nils Lid Hjort, the focused information criterion for model selection. With Hjort, she is the author of the book ''Model Selection and Model Averaging'' (Cambridge University Press, 2008). Education and career Claeskens earned a licentiate in mathematics at the University of Antwerp in 1995. In 1999, she earned a master's degree in biostatistics and Ph.D. in mathematics, at Limburgs Universitair Centrum (now the University of Hasselt); her dissertation, supervised by Marc Aerts, was ''Smoothing Techniques and Bootstrap Methods for Multiparameter Likelihood''. She did postdoctoral research at the Australian Na ...
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American Journal Of Mathematical And Management Sciences
American(s) may refer to: * American, something of, from, or related to the United States of America, commonly known as the "United States" or "America" ** Americans, citizens and nationals of the United States of America ** American ancestry, people who self-identify their ancestry as "American" ** American English, the set of varieties of the English language native to the United States ** Native Americans in the United States, indigenous peoples of the United States * American, something of, from, or related to the Americas, also known as "America" ** Indigenous peoples of the Americas * American (word), for analysis and history of the meanings in various contexts Organizations * American Airlines, U.S.-based airline headquartered in Fort Worth, Texas * American Athletic Conference, an American college athletic conference * American Recordings (record label), a record label previously known as Def American * American University, in Washington, D.C. Sports teams Soccer * Ba ...
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Biometrika
''Biometrika'' is a peer-reviewed scientific journal published by Oxford University Press for thBiometrika Trust The editor-in-chief is Paul Fearnhead (Lancaster University). The principal focus of this journal is theoretical statistics. It was established in 1901 and originally appeared quarterly. It changed to three issues per year in 1977 but returned to quarterly publication in 1992. History ''Biometrika'' was established in 1901 by Francis Galton, Karl Pearson, and Raphael Weldon to promote the study of biometrics. The history of ''Biometrika'' is covered by Cox (2001). The name of the journal was chosen by Pearson, but Francis Edgeworth insisted that it be spelt with a "k" and not a "c". Since the 1930s, it has been a journal for statistical theory and methodology. Galton's role in the journal was essentially that of a patron and the journal was run by Pearson and Weldon and after Weldon's death in 1906 by Pearson alone until he died in 1936. In the early days, the American ...
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Watanabe–Akaike Information Criterion
In statistics, the widely applicable information criterion (WAIC), also known as Watanabe–Akaike information criterion, is the generalized version of the Akaike information criterion (AIC) onto singular statistical models. Widely applicable Bayesian information criterion (WBIC) is the generalized version of Bayesian information criterion (BIC) onto singular statistical models. WBIC is the average log likelihood function over the posterior distribution with the inverse temperature > 1/log ''n'' where ''n'' is the sample size. Both WAIC and WBIC can be numerically calculated without any information about a true distribution. See also *Akaike information criterion *Bayesian information criterion *Deviance information criterion *Hannan–Quinn information criterion *Shibata information criterion Shibata may refer to: Places * Shibata, Miyagi, a town in Miyagi Prefecture * Shibata District, Miyagi, a district in Miyagi Prefecture * Shibata, Niigata, a city in Niigata ...
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Jensen–Shannon Divergence
In probability theory and statistics, the Jensen– Shannon divergence is a method of measuring the similarity between two probability distributions. It is also known as information radius (IRad) or total divergence to the average. It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it always has a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen–Shannon distance. Definition Consider the set M_+^1(A) of probability distributions where A is a set provided with some σ-algebra of measurable subsets. In particular we can take A to be a finite or countable set with all subsets being measurable. The Jensen–Shannon divergence (JSD) M_+^1(A) \times M_+^1(A) \rightarrow [0,\infty) is a symmetrized and smoothed version of the Kullback–Leibler divergence D(P \parallel Q). It is defined by :(P \parallel Q)= \fracD(P \parallel M)+\fracD(Q \parallel M), ...
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