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- W2056589181 abstract "Let (T1, x1), (T2, x2), …, (Tn, xn) be a sample from a multivariate normal distribution where Ti are (unobservable) random variables and xi are random vectors in Rk. If the sample is either independent and identically distributed or satisfies a multivariate components of variance model, then the probability of correctly ordering {Ti} is maximized by ranking according to the order of the best linear predictors {E(Ti|xi)}. Furthermore, it orderings are chosen according to linear functions {b′xi} then the conditional probability of correct order given (Ti = t1; i = 1, …, n) is maximized when b′xi is the best linear predictor. Examples are given to show that linear predictors may not be optimal and that using a linear combination other that the best linear predictor may give a greater probability of correctly ordering {Ti} if {(Ti, xi)} are independent but not identically distributed, or if the distributions are not normal." @default.
- W2056589181 created "2016-06-24" @default.
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- W2056589181 date "1982-06-01" @default.
- W2056589181 modified "2023-10-11" @default.
- W2056589181 title "Maximizing the probability of correctly ordering random variables using linear predictors" @default.
- W2056589181 cites W2017915933 @default.
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- W2056589181 doi "https://doi.org/10.1016/0047-259x(82)90019-7" @default.
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