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Covariates and Covariance Analyses

2012
A confounding variable is the bane of every clinician’s and researcher’s existence. A confounding variable is anything that can affect (or effect) an outcome measure, principally performance on neuropsychological testing, and is not of primary relevance in clinical practice or experimentation.
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Covariant specification

ACM SIGPLAN Notices, 1994
Covariant specification is the process in a subclass to narrow the varying scope of an instance variable, or the varying scope of the input or output interface of an operation which has already been specified in the superclass. Covariant specification has been considered problematic and many object-oriented languages have restrictions on it.
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COVARIANT ENERGY IN MECHANICS

International Journal of Geometric Methods in Modern Physics, 2007
Different notions of mechanical energy and conservation laws are considered and discussed from the viewpoint of transformation laws with respect to changes of reference. Here, in particular, we shall consider and investigate the behavior of suitable conservation laws in non-inertial frames.
CARINI, Manuela   +2 more
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Outlyingness Weighted Covariation

SSRN Electronic Journal, 2008
Quadratic covariation is a popular descriptive measure for the volatility of a multivariate price process. It is consistently estimated by the sum of outer products of high-frequency returns. The proposed realized outlyingness weighted covariation (ROWCov) is a weighted sum of outer products of high-frequency returns and downweights returns that ...
Boudt, Kris   +2 more
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The Analysis of Covariance

Biometrics, 1948
THE WHOLE OF this discussion is based on the data of a single experiment, the details of which have been published under the title "The Effect of Atropine and Quinidine Sulphate on Atrophy and Fibrillation in Deniervated Skeletal Muscle." [1] For the present, we may adopt the view that the experiment was conducted to compare the effects of four ...
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Mixtures of inverse covariances

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003
We describe a model which approximates full covariances in a Gaussian mixture while reducing significantly both the number of parameters to estimate and the computations required to evaluate the Gaussian likelihoods. In this model, the inverse covariance of each Gaussian in the mixture is expressed as a linear combination of a small set of prototype ...
Vincent Vanhoucke, Ananth Sankar
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Adding additional covariates and the Analysis of Covariance

2010
Suppose that having fitted the regression model $$\vec{y} = X\beta + \epsilon, $$ (M0) we wish to introduce qadditional explanatory variables into our model. The augmented regression model, M A , say becomes $$\vec{y} = X\beta + Z\gamma + \epsilon.
N. H. Bingham, John M. Fry
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Normal covariances

Kybernetika, 1988
Covariances R(s,t) are called normal if they can be written in the form \[ R(s,t)=\int^{\infty}_{-\infty}\int^{\infty}_{- \infty}e^{\lambda (s+t)}e^{i\mu (s-t)}dF(\lambda,\mu),\quad (s,t)\in R^ 2. \] Some properties and characteristics of normal covariances are proved (in addition to previous results of the author): 1) they are continuous on \(R^ 2 ...
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Covariance Selection

Advances in Applied Probability, 1978
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Genetic Covariance

2013
Szurmant, Hendrik, Weigt, Martin
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