Description Usage Arguments Details Value Author(s) References Examples
View source: R/lorenz.exp.test.R
Performs Lorenz test for the composite hypothesis of exponentiality, see e.g. Gail and Gastwirth (1978).
1 | lorenz.exp.test(x, p=0.5, simulate.p.value=FALSE, nrepl=2000)
|
x |
a numeric vector of data values. |
p |
a parameter of the test (see below). |
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 Lorenz test for exponentiality is based on the following statistic:
L = ∑_{i=1}^{np}{X_{(i)}}/∑_{i=1}^n{X_{(i)}}
The statistic √{n}(L-p-(1-p)\log(1-p)) is asymptotically standard normal.
A list with class "htest" containing the following components:
statistic |
the value of the Lorenz statistic. |
p.value |
the p-value for the test. |
method |
the character string "Lorenz test for exponentiality". |
data.name |
a character string giving the name(s) of the data. |
Alexey Novikov and Ruslan Pusev
Gail, M.H. and Gastwirth, J.L. (1978): A scale-free goodness-of-fit test for the exponential distribution based on the Lorenz curve. — Journal of the American Statistical Association, vol. 73, pp. 787–793.
1 2 | lorenz.exp.test(rexp(100))
lorenz.exp.test(rchisq(100,7))
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