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Overfitting
In statistics, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably".[1] An overfitted model is a statistical model that contains more parameters than can be justified by the data.[2] The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e. the noise) as if that variation represented underlying model structure.[3]:45 Underfitting occurs when a statistical model cannot adequately capture the underlying structure of the data. An underfitted model is a model where some parameters or terms that would appear in a correctly specified model are missing.[2] Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model will tend to have poor predictive performance. Overfitting
Overfitting
and underfitting can occur in machine learning, in particular
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Polynomial
In mathematics, a polynomial is an expression consisting of variables (also called indeterminates) and coefficients, that involves only the operations of addition, subtraction, multiplication, and non-negative integer exponents of variables. An example of a polynomial of a single indeterminate x is x2 − 4x + 7. An example in three variables is x3 + 2xyz2 − yz + 1. Polynomials appear in a wide variety of areas of mathematics and science. For example, they are used to form polynomial equations, which encode a wide range of problems, from elementary word problems to complicated problems in the sciences; they are used to define polynomial functions, which appear in settings ranging from basic chemistry and physics to economics and social science; they are used in calculus and numerical analysis to approximate other functions
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Cambridge University Press
Cambridge
Cambridge
University Press (CUP) is the publishing business of the University of Cambridge. Granted letters patent by King Henry VIII in 1534, it is the world's oldest publishing house and the second-largest university press in the world (after Oxford University Press).[2][3] It also holds letters patent as the Queen's Printer.[4] The press's mission is "To further the University's mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence."[5] Cambridge
Cambridge
University Press is a department of the University of Cambridge
Cambridge
and is both an academic and educational publisher. With a global sales presence, publishing hubs, and offices in more than 40 countries, it publishes over 50,000 titles by authors from over 100 countries
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Freedman's Paradox
In statistical analysis, Freedman's paradox,[1][2] named after David Freedman, describes a problem in model selection whereby predictor variables with no explanatory power can appear artificially important. Freedman demonstrated (through simulation and asymptotic calculation) that this is a common occurrence when the number of variables is similar to the number of data points. Recently, new information-theoretic estimators have been developed in an attempt to reduce this problem,[3] in addition to the accompanying issue of model selection bias,[4] whereby estimators of predictor variables that have a weak relationship with the response variable are biased. References[edit]^ Freedman, D. A. (1983) "A note on screening regression equations." The American Statistician, 37, 152–155. ^ Freedman, Laurence S.; Pee, David (November 1989). "Return to a Note on Screening Regression Equations". The American Statistician. 43 (4): 279–282. doi:10.2307/2685389.  ^ Lukacs, P
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The Journal Of Investing
The Journal of Investing is a quarterly academic journal that covers research on portfolio management, asset allocation, performance measurement, benchmarking, mutual funds, investing strategies such as 130/30 funds, global allocation, and practical investment ideas and portfolio strategies for the institutional buy-side such as pension funds. The journal is listed among a select list of finance journals and has contributors from universities as well as research-centric investment professionals in asset management. The editor-in-chief is Brian R
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Digital Object Identifier
In computing, a Digital Object Identifier or DOI is a persistent identifier or handle used to uniquely identify objects, standardized by the International Organization for Standardization
International Organization for Standardization
(ISO).[1] An implementation of the Handle System,[2][3] DOIs are in wide use mainly to identify academic, professional, and government information, such as journal articles, research reports and data sets, and official publications though they also have been used to identify other types of information resources, such as commercial videos. A DOI aims to be "resolvable", usually to some form of access to the information object to which the DOI refers. This is achieved by binding the DOI to metadata about the object, such as a URL, indicating where the object can be found. Thus, by being actionable and interoperable, a DOI differs from identifiers such as ISBNs and ISRCs which aim only to uniquely identify their referents
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Journal Of Chemical Information And Modeling
The Journal of Chemical Information and Modeling is a peer-reviewed scientific journal published by the American Chemical Society. It was established in 1961 as the Journal of Chemical Documentation, renamed in 1975 to Journal of Chemical Information and Computer Sciences, and obtained its current name in 2005. The journal covers the fields of chemical informatics and molecular modeling. The editor-in-chief is Kenneth M. Merz Jr. (Michigan State University). Abstracting and indexing[edit] The journal is abstracted and indexed in:Chemical Abstracts Service Scopus[1] ProQuest
ProQuest
databases Science Citation Index[2] Current Contents/Physical, Chemical & Earth Sciences[2] Index Medicus/MEDLINE/PubMed.[3]References[edit]^ " Scopus title list". Elsevier. Archived from the original (Microsoft Excel) on 2013-12-02. Retrieved 2014-09-18.  ^ a b "Master Journal List". Intellectual Property & Science. Thomson Reuters
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BioData Mining
BioData Mining
BioData Mining
is a peer-reviewed open access scientific journal covering data mining methods applied to computational biology published by BioMed Central.[1] It was established in 2008.[2] Its current impact factor is 1.557.[3] Abstracting and indexing[edit] The journal is abstracted and indexed by:Biological Abstracts BIOSIS Chemical Abstracts Service Embase EmBiology EmCare MEDLINE/PubMed Science Citation Index Expanded ScopusReferences[edit]^ Scimagojr.com ^
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PubMed Central
PubMed
PubMed
Central (PMC) is a free digital repository that archives publicly accessible full-text scholarly articles that have been published within the biomedical and life sciences journal literature. As one of the major research databases within the suite of resources that have been developed by the National Center for Biotechnology Information (NCBI), PubMed
PubMed
Central is much more than just a document repository
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PubMed Identifier
PubMed
PubMed
is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The United States National Library of Medicine
United States National Library of Medicine
(NLM) at the National Institutes of Health
National Institutes of Health
maintains the database as part of the Entrez
Entrez
system of information retrieval. From 1971 to 1997, MEDLINE online access to the MEDLARS Online computerized database primarily had been through institutional facilities, such as university libraries
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OxfordDictionaries.com
OxfordDictionaries.com, originally titled Oxford Dictionaries Online (ODO) and rebranded Oxford English Living Dictionaries (OELD) in 2017, is an online dictionary produced by the Oxford University Press (OUP)[1] publishing house, a department of the University of Oxford, which also publishes a number of print dictionaries, among other works. The database includes the contents of Oxford Dictionary
Dictionary
of English, New Oxford American Dictionary, Oxford Thesaurus of English, Oxford American Writer's Thesaurus, and the website also provides English grammar and usage resources.[2] The database provides both "world" and American English
American English
as separate lexicons; while most entries are the same, some significantly differ to reflect distinctions in usage
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Google Scholar
Google
Google
Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines
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Statistical Model
A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of some sample data and similar data from a larger population. A statistical model represents, often in considerably idealized form, the data-generating process. The assumptions embodied by a statistical model describe a set of probability distributions, some of which are assumed to adequately approximate the distribution from which a particular data set is sampled. The probability distributions inherent in statistical models are what distinguishes statistical models from other, non-statistical, mathematical models. A statistical model is usually specified by mathematical equations that relate one or more random variables and possibly other non-random variables
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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).[1] 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).[2] 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);[3] her dissertation, supervised by Marc Aerts, was Smoothing Techniques and Bootstrap Methods for Multiparameter Likelihood.[3][4] She did postdoctoral research at the Australian National University
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Nils Lid Hjort
Nils Lid Hjort (born 12 January 1953) is a Norwegian statistician, and 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), and model selection (focused information criteria and model averaging). 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
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University Of Texas At Austin
A university (Latin: universitas, "a whole") is an institution of higher (or tertiary) education and research which awards academic degrees in various academic disciplines
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