Description Usage Arguments Details Value Author(s) References Examples
View source: R/epstein.exp.test.R
Performs Epstein test for the composite hypothesis of exponentiality, see e.g. Ascher (1990).
1 | epstein.exp.test(x, simulate.p.value=FALSE, nrepl=2000)
|
x |
a numeric vector of data values. |
simulate.p.value |
a logical value indicating whether to compute p-values by Monte Carlo simulation. |
nrepl |
the number of replications in Monte Carlo simulation. |
The test is based on the following statistic:
EPS_n =\frac{2n≤ft(\log≤ft(n^{-1}∑_{i=1}^nD_i\right)-n^{-1}∑_{i=1}^n\log(D_i)\right)}{1+(n+1)/(6n)},
where D_i=(n-i+1)(X_{(i)}-X_{(i-1)}), X_{(0)}=0 and X_{(1)}≤q…≤q X_{(n)} are the order statistics. Under exponentiality, EPS is approximately distributed as a chi-square with n-1 degrees of freedom.
A list with class "htest" containing the following components:
statistic |
the value of the test statistic. |
p.value |
the p-value for the test. |
method |
the character string "Epstein test for exponentiality". |
data.name |
a character string giving the name(s) of the data. |
Alexey Novikov, Ruslan Pusev and Maxim Yakovlev
Ascher, S. (1990): A survey of tests for exponentiality. — Communications in Statistics – Theory and Methods, vol. 19, pp. 1811–1825.
1 2 | epstein.exp.test(rexp(100))
epstein.exp.test(rweibull(100,2))
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