Description Usage Arguments Details Value Author(s) See Also
View source: R/test.model.MSAR.R
Performs bootstrap statistical tests to validate MSAR models. Marginal distribution, auto correlation function and up-crossings are considered. For each of them the tests statistic computed from observations is compared to the distribution of the satistics corresponding to the MSAR model.
1 | test.model.MSAR(data,simu,lag=NULL,id=1,u=NULL)
|
data |
observed (or reference) time series, array of dimension T*N.samples*d |
simu |
simulated time series, array of dimension T*N.sim*d. N.sim have to be K*N.samples with K large enough (for instance, K=100) |
lag |
maximum lag for auto-correlation functions. |
id |
considered component. It is usefull when data is multivariate. |
u |
considered levels for up crossings |
Test statistics Marginal distribution:
S = \int_{-∞}^{∞} ≤ft| F_n(x)-F(x) \right| dx
Marginal distribution, based on Anderson Darling statistic:
S = \int_{-∞}^{∞} ≤ft| \frac{F_n(x)-F(x)}{F(x)(1-F(x))} \right| dx
Correlation function:
S = \int_0^L≤ft|C_n(l)-C(l)\right|dl
Number of up crossings:
S = \int_{-∞}^{∞}≤ft|E_n(N_u)-E(N_u)\right|du
Returns a list including
StaDist |
statistics of marginal distributions, based on Smirnov like statistics |
..$dd |
test statistic |
..$q.dd |
quantiles .05 and .95 of the distribution of the test statistic under the null hypothesis |
..$p.value |
p value |
Cor |
statistics of correlation functions |
..$dd |
test statistic |
..$q.dd |
quantiles .05 and .95 of the distribution of the test statistic under the null hypothesis |
..$p.value |
p value |
ENu |
statistics of intensity of up crossings |
..$dd |
test statistic |
..$q.dd |
quantiles .05 and .95 of the distribution of the test statistic under the null hypothesis |
..$p.value |
p value |
AD |
statistics of marginal distributions, based on Anderson Darling statistics |
..$dd |
test statistic |
..$q.dd |
quantiles .05 and .95 of the distribution of the test statistic under the null hypothesis |
..$p.value |
p value |
Valerie Monbet, valerie.monbet@univ-rennes1.fr
valid_all, test.model.MSAR
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