| IARkalman {iAR} | R Documentation |
Maximum Likelihood Estimation of the IAR Model via Kalman Recursions
Description
Maximum Likelihood Estimation of the IAR model parameter phi. The estimation procedure uses the Kalman Filter to find the maximum of the likelihood.
Usage
IARkalman(y, st, delta = 0, zero.mean = TRUE, standardized = TRUE)
Arguments
y |
Array with the time series observations. |
st |
Array with the irregular observational times. |
delta |
Array with the measurements error standard deviations. |
zero.mean |
logical; if TRUE, the array y has zero mean; if FALSE, y has a mean different from zero. |
standardized |
logical; if TRUE, the array y is standardized; if FALSE, y contains the raw time series. |
Value
A list with the following components:
phi MLE of the phi parameter of the IAR model.
ll Value of the negative log likelihood evaluated in phi.
References
Eyheramendy S, Elorrieta F, Palma W (2018). “An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves.” Monthly Notices of the Royal Astronomical Society, 481(4), 4311–4322. ISSN 0035-8711, doi: 10.1093/mnras/sty2487, https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf.
See Also
gentime, IARsample, arima,IARphikalman
Examples
set.seed(6714)
st<-gentime(n=100)
y<-IARsample(phi=0.99,st=st,n=100)
y<-y$series
phi=IARkalman(y=y,st=st)$phi
print(phi)