R: Exponential empirical likelihood for a one sample mean vector...

Exponential empirical likelihood for a one sample mean vector hypothesis testing {Rfast} | R Documentation

## Exponential empirical likelihood for a one sample mean vector hypothesis testing

### Description

Exponential empirical likelihood for a one sample mean vector hypothesis testing.

### Usage

    mv.eeltest1(x, mu, tol = 1e-06)

### Arguments

`x` | A matrix containing Euclidean data.
`mu` | The hypothesized mean vector.
`tol` | The tolerance value used to stop the Newton-Raphson algorithm.

### Details

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).

### Value

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.

### Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr>.

### References

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.

### See Also

`james, mv.eeltest2`

### Examples

    x <- Rfast::rmvnorm(100, numeric(10), diag( rexp(10, 0.5) ) )
    res<-mv.eeltest1(x, numeric(10) )

[Package Rfast version 2.0.4 Index]