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- Mean_dependence abstract "In probability theory, a random variable Y is said to be mean independent of random variable X if and only if E(Y|X) = E(Y) for all x such that ƒ1(x) is not equal to zero. Y is said to be mean dependent if E(Y|X) ≠ μ(y) for some x such that ƒ1(x) is not equal to zero.According to Cameron and Trivedi (2009, p. 23) and Wooldridge (2010, pp. 54, 907), Stochastic independence implies mean independence, but the converse is not necessarily true.Moreover, mean independence implies uncorrelation while the converse is not necessarily true.The concept of mean independence is often used in econometrics to have a middle ground between the strong assumption of independent distributions and the weak assumption of uncorrelated variables of a pair of random variables and .If X, Y are two different random variables such that X is mean independent of Y and Z=f(X), which means that Z is a function only of X, then Y and Z are mean independent.".
- Mean_dependence wikiPageID "24836552".
- Mean_dependence wikiPageRevisionID "564584291".
- Mean_dependence hasPhotoCollection Mean_dependence.
- Mean_dependence subject Category:Probability_theory.
- Mean_dependence subject Category:Statistical_terminology.
- Mean_dependence comment "In probability theory, a random variable Y is said to be mean independent of random variable X if and only if E(Y|X) = E(Y) for all x such that ƒ1(x) is not equal to zero. Y is said to be mean dependent if E(Y|X) ≠ μ(y) for some x such that ƒ1(x) is not equal to zero.According to Cameron and Trivedi (2009, p. 23) and Wooldridge (2010, pp.".
- Mean_dependence label "Mean dependence".
- Mean_dependence sameAs m.080kvgf.
- Mean_dependence sameAs Q6803618.
- Mean_dependence sameAs Q6803618.
- Mean_dependence wasDerivedFrom Mean_dependence?oldid=564584291.
- Mean_dependence isPrimaryTopicOf Mean_dependence.