sarma.est {DCSmooth} | R Documentation |
Estimation of an SARMA-process
Description
Parametric Estimation of an -process on a
lattice.
Usage
sarma.est(Y, method = "HR", model_order = list(ar = c(1, 1), ma = c(1, 1)))
qarma.est(Y, model_order = list(ar = c(1, 1), ma = c(1, 1)))
Arguments
Y |
A numeric matrix that contains the demeaned observations of the random field or functional time-series. |
method |
Method used for estimation of the parameters. One of |
model_order |
A list containing the orders of the SARMA model in the
form |
Value
The function returns an object of class "sarma"
including
Y | The matrix of observations, inherited from input. |
innov The estimated innovations. |
|
model | The estimated model consisting of the coefficient
matrices ar and ma and standard deviation of innovations
sigma . |
stnry | An logical variable indicating whether the estimated model is stationary. |
Details
The MA- and AR-parameters of a top-left quadrant ARMA process are estimated
by the specified method. The lag-orders of the are given by
, where
are the lags over the rows and
are the lags over the columns. The estimation process is based on the model
.
See Also
Examples
# See vignette("DCSmooth") for examples and explanation
## simulation of SARMA process
ma <- matrix(c(1, 0.2, 0.4, 0.1), nrow = 2, ncol = 2)
ar <- matrix(c(1, 0.5, -0.1, 0.1), nrow = 2, ncol = 2)
sigma <- 0.5
sarma_model <- list(ar = ar, ma = ma, sigma = sigma)
sarma_simulated <- sarma.sim(100, 100, model = sarma_model)
sarma_simulated$model
## estimation of SARMA process
sarma.est(sarma_simulated$Y)$model
sarma.est(sarma_simulated$Y,
model_order = list(ar = c(1, 1), ma = c(1, 1)))$model
[Package DCSmooth version 1.1.2 Index]