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Variance, Analysis of

2008
Analysis of variance (ANOVA) represents a set of models that can be fit to data, and also a set of methods for summarizing an existing fitted model. We first consider ANOVA as it applies to classical linear models (the context for which it was originally devised; Fisher, 1925) and then discuss how ANOVA has been extended to generalized linear models ...
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The Analysis of Variance

1981
In earlier chapters we have defined the model linear in the parameters, commonly called the linear model, and discussed the theory of estimating parameters of the model by the method of least squares. Our applications of the theory so far have been to simple linear regression, polynomial regression and multiple regression. However, there is a very wide
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g-variance

Acta Mathematica Sinica, English Series, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Variants of Variance

JAMA: The Journal of the American Medical Association, 1977
The unitary facade of angina pectoris was cracked 18 years ago when Prinzmetal et al 1 chipped off a "variant" form that departs from the classical pattern of Heberden angina of effort in several respects. The pain occurs at rest and is often accompanied by arrhythmias or heart block; it is associated with electrocardiographic ST-segment elevations ...
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Analysis of Variance

Circulation, 2008
Analysis of variance (ANOVA) is a statistical technique to analyze variation in a response variable (continuous random variable) measured under conditions defined by discrete factors (classification variables, often with nominal levels). Frequently, we use ANOVA to test equality among several means by comparing variance among groups relative to ...
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Synthesis of Variance

Psychometrika, 1941
The distribution of a linear combination of two statistics distributed as is Chi-square is studied. The degree of approximation involved in assuming a Chi-square distribution is illustrated for several representative cases. It is concluded that the approximation is sufficiently accurate to use in many practical applications.
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THE MEAN AND VARIANCE OF THE MEAN-VARIANCE DECISION RULE

1987
The sampling properties of the mean-variance decision vector are studied. We sho,v that, when the parameters of yield distributions are uncertain, risk in estimation causes bias in these decisions and large variances. Especially with small samples, decisions based on estimated parameters can be very poor estimates of optimal behavior.
Chalfant, James A.   +5 more
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Variance trading and market price of variance risk

Journal of Econometrics, 2010
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The Variance of Variance

1991
HOWARD MARK, JERRY WORKMAN
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