fitMisRepARMA {MisRepARMA} | R Documentation |
Fits an ARMA model to misreported time series data.
fitMisRepARMA(y, tol, B, p_AR, q_MA, covars=NULL, misReport="U", ...)
y |
a numeric vector or time series giving the original data. |
tol |
tolerance limit to stop the iterative algorithm. |
B |
the number of bootstrap series to compute. |
p_AR |
order of the AR part. |
q_MA |
order of the MA part. |
covars |
matrix of explanatory variables. Its default value is |
misReport |
direction of misreporting issue. Its default value is |
... |
additional arguments to pass to |
The model based resampling scheme with B
bootstrap resamples is computed. This
An object of class fitMisRepARMA
with the following elements is returned:
data
: The original time series.
t0
: The results of applying statistic to the original series.
t
: Estimates on each replicated time series.
call
: The original call to tsboot.
David Moriña, Amanda Fernández-Fontelo, Alejandra Cabaña, Pedro Puig
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
Kunsch, H.R. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17, 1217–1241.
Moriña, D., Fernández-Fontelo, A., Cabaña, A., Puig, P. (2021): New statistical model for misreported data with application to current public health challenges. arXiv preprint (https://arxiv.org/pdf/2003.09202.pdf)
Politis, D.N. and Romano, J.P. (1994) The stationary bootstrap. Journal of the American Statistical Association, 89, 1303–1313.
MisRepARMA-package
, reconstruct
### Simulate underreported time series data
set.seed(12345)
x <- arima.sim(model=list(ar=0.4), n=50)
ind <- rbinom(50, 1, 0.6)
y <- ifelse(ind==0, x, x*0.3)
mod <- fitMisRepARMA(y, 1e-6, 3, 0.05, 1, 0, covars=NULL, misReport="U")