Results 1  10
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2,936
Least angle regression
, 2004
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1326 (37 self)
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The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope
A Covariant Holographic Entanglement Entropy Proposal
, 2008
"... With an aim towards understanding the timedependence of entanglement entropy in generic quantum field theories, we propose a covariant generalization of the holographic entanglement entropy proposal of hepth/0603001. Apart from providing several examples of possible covariant generalizations, we s ..."
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Cited by 140 (20 self)
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With an aim towards understanding the timedependence of entanglement entropy in generic quantum field theories, we propose a covariant generalization of the holographic entanglement entropy proposal of hepth/0603001. Apart from providing several examples of possible covariant generalizations, we
Misunderstanding analysis of covariance
 Journal of Abnormal Psychology
, 2001
"... Despite numerous technical treatments in many venues, analysis of covariance (ANCOVA) remains a widely misused approach to dealing with substantive group differences on potential covariates, particularly in psychopathology research. Published articles reach unfounded conclusions, and some statistic ..."
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Cited by 115 (7 self)
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Despite numerous technical treatments in many venues, analysis of covariance (ANCOVA) remains a widely misused approach to dealing with substantive group differences on potential covariates, particularly in psychopathology research. Published articles reach unfounded conclusions, and some
A Theory Of Inferred Causation
, 1991
"... This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything ..."
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Cited by 254 (38 self)
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useful about the causal mechanisms that underly the observations. We propose a minimalmodel semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a
Correction of logistic regression relative risk estimates and confidence intervals for systematic withinperson measurement error. Stat Med
"... Frequently, covariates used in a logistic regression are measured with error. The authors previously described the correction of logistic regression relative risk estimates for measurement error in one or more covariates when a "gold standard " is available for exposure assessment. For som ..."
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Cited by 220 (18 self)
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. For some exposures (e.g., serum cholesterol), no gold standard exists, and one must assess measurement error via a reproducibility substudy. In this paper, the authors present measurement error methods for logistic regression when there is error (possibly correlated) in one or more covariates and one has
General MultiLevel Linear Modelling for Group Analysis in FMRI
 NeuroImage
, 2003
"... This paper discusses general modelling of multisubject and/or multisession FMRI data. In particular, we show that a twolevel mixedeffects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the singlesession level) can be made equivale ..."
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Cited by 209 (8 self)
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of such models are given in the paper. Using numerical simulations based on typical first level covariance structures from real FMRI data we demonstrate that by taking into account lowerlevel covariances and heterogeneity a substantial increase in higherlevel Zscore is possible. 1
Covariance fields
, 2008
"... We introduce and study covariance fields of distributions on a Riemannian manifold. At each point on the manifold, covariance is defined to be a symmetric and positive definite (2,0)tensor. Its product with the metric tensor specifies a linear operator on the respected tangent space. Collectively, ..."
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Cited by 2 (1 self)
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, these operators form a covariance operator field. We show that, in most circumstances, covariance fields are continuous. We also solve the inverse problem: recovering distribution from a covariance field. Surprisingly, this is not possible on Euclidean spaces. On nonEuclidean manifolds however, covariance fields
Recognizing text genres with simple metrics using discriminant analysis
 In Proceedings of the 15th Conference on Computational Linguistics
, 1994
"... A simple method for categorizing texts into predetermined text genre categories using the statistical standard technique of discriminant analysis is demonstrated with application to the Brown corpus. Discriminant analysis makes it possible use a large number of parameters that may be specific for a ..."
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Cited by 194 (15 self)
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A simple method for categorizing texts into predetermined text genre categories using the statistical standard technique of discriminant analysis is demonstrated with application to the Brown corpus. Discriminant analysis makes it possible use a large number of parameters that may be specific
Candid covariancefree incremental principal component analysis
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2003
"... Abstract—Appearancebased image analysis techniques require fast computation of principal components of highdimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariancefree IPCA (CCIPCA), used to compute the principal components ..."
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Cited by 83 (9 self)
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Abstract—Appearancebased image analysis techniques require fast computation of principal components of highdimensional image vectors. We introduce a fast incremental principal component analysis (IPCA) algorithm, called candid covariancefree IPCA (CCIPCA), used to compute the principal
Covariant Thermodynamics and
, 1995
"... We discuss a cosmological Friedmann model modified by inclusion of offshell matter which has an equation of state p,ρ ∝ T 5, p = 1/4ρ. Such matter is shown to have energy density comparable with that of noninteracting radiation at temperatures of the order of the Hagedorn temperature, ∼ 10 12 K, i ..."
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, indicating the possibility of a phase transition. It is argued that the T 5phase, or an admixture, lies below the hightemperature T 4phase.
Results 1  10
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2,936