Exponential empirical likelihood for a one sample mean vector hypothesis testing
Exponential empirical likelihood for a one sample mean vector hypothesis testing.
mv.eeltest1(x, mu, tol = 1e-06)
x |
A matrix containing Euclidean data. |
mu |
The hypothesized mean vector. |
tol |
The tolerance value used to stop the Newton-Raphson algorithm. |
Multivariate hypothesis test for a one sample mean vector. This is a non parametric test and it works for univariate and multivariate data. The p-value is currently computed only asymptotically (no bootstrap calibration at the moment).
A list including:
p |
The estimated probabiities. |
lambda |
The value of the Lagrangian parameter λ. |
iters |
The number of iterations required by the newton-Raphson algorithm. |
info |
The value of the log-likelihood ratio test statistic along with its corresponding p-value. |
Michail Tsagris
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr>.
Jing Bing-Yi and Andrew TA Wood (1996). Exponential empirical likelihood is not Bartlett correctable. Annals of Statistics 24(1): 365-369.
Owen A. B. (2001). Empirical likelihood. Chapman and Hall/CRC Press.
x <- Rfast::rmvnorm(100, numeric(10), diag( rexp(10, 0.5) ) ) res<-mv.eeltest1(x, numeric(10) )
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