Description Usage Arguments Details Value Author(s) References Examples
View source: R/moran.exp.test.R
Performs Moran test for the composite hypothesis of exponentiality, see e.g. Moran (1951) and Tchirina (2005).
1 | moran.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 Moran test for exponentiality is based on the following statistic:
T_n^+ = γ + \frac{1}{n}∑_{i=1}^n{\log\frac{X_i}{\overline{X}}},
where γ is Euler-Mascheroni constant. The statistic is asymptotically normal:
√{n}\,T_n^+ \to N≤ft(0,\frac{π^2}{6} - 1\right).
A list with class "htest" containing the following components:
statistic |
the value of the Moran statistic. |
p.value |
the p-value for the test. |
method |
the character string "Moran test for exponentiality". |
data.name |
a character string giving the name(s) of the data. |
Alexey Novikov and Ruslan Pusev
Moran, P.A.P. (1951): The random division of an interval–Part II. — Journal of the Royal Statistical Society. Series B (Methodological), vol. 13, pp. 147-150.
Tchirina, A.V. (2005): Bahadur efficiency and local optimality of a test for exponentiality based on the Moran statistics. — Journal of Mathematical Sciences, vol. 127, No. 1, pp. 1812–1819.
1 2 | moran.exp.test(rexp(100))
moran.exp.test(rchisq(100,3))
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