Results 241 to 250 of about 1,051,485 (290)

Adjusting for demographic covariates by the analysis of covariance

Journal of Clinical and Experimental Neuropsychology, 1992
(1992). Adjusting for demographic covariates by the analysis of covariance. Journal of Clinical and Experimental Neuropsychology: Vol. 14, No. 6, pp. 981-982.
N C, Berman, S W, Greenhouse
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Misclassification of covariates

Statistics in Medicine, 1991
AbstractIn the context of reported data on asthma mortality, we examine two types of covariate misclassification. The first type is non‐differential misclassification, in which the proportion of subjects misclassified is invariant over exposure and disease status. Building on work by Cox and Elwood and by Blettner and Wahrendorf, we find that the range
A M, Walker, S F, Lanes
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Covariate-free and Covariate-dependent Reliability

Psychometrika, 2016
Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population ...
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COVARIANCE EFFECT

Macroeconomic Dynamics, 2002
If one drops the strong assumption that firms and households know all of the relevant parameters, and instead models agents as learning these parameters, the estimated parameters become random variables. Taking expectations several periods into the future may then involve taking the expectation of a product of random variables.
Moore, Bartholomew, Schaller, Huntley
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Covariance and Pseudo-Covariance of Complex Uncertain Variables

Journal of Intelligent & Fuzzy Systems, 2019
Covariance is a measure to characterize the joint variability of two complex uncertain variables. Since, calculating covariance is not easy based on uncertain measure, we present two formulas for covariance and pseudo covariance of complex uncertain variables.
Rong Gao, Hamed Ahmadzade, Mojtaba Esfahani   +2 more
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Selection and Covariance

Nature, 1970
THIS is a preliminary communication describing applications to genetical selection of a new mathematical treatment of selection in general.
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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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ON THE COVARIANCE OF THE PERIODOGRAM

Journal of Time Series Analysis, 1982
Abstract. The paper discusses the covariance of the periodogram from a zero mean fourth order stationary stochastic process. The fourth order cumulant term appearing in the covariance is shown to be a convolution between the fourth order cumulant spectrum and a bounded approximate identity, and this gives precise results about its asymptotic behaviour.
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Kinship and covariance

Journal of Theoretical Biology, 1981
Abstract Price's (1970) covariance theorem can be used to derive an expression for gene frequency change in kin selection models in which the fitness effect of an act is independent of the genotype of the recipient. This expression defines a coefficient of relatedness which subsumes r (Wright, 1922) , b (Hamilton, 1972) , ρ (Orlove & Wood, 1978)
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