# BIARkalman: Maximum Likelihood Estimation of the BIAR Model via Kalman... In iAR: Irregularly Observed Autoregressive Models

 BIARkalman R Documentation

## Maximum Likelihood Estimation of the BIAR Model via Kalman Recursions

### Description

Maximum Likelihood Estimation of the BIAR model parameters phiR and phiI. The estimation procedure uses the Kalman Filter to find the maximum of the likelihood.

### Usage

```BIARkalman(
y1,
y2,
t,
delta1 = 0,
delta2 = 0,
zero.mean = "TRUE",
niter = 10,
seed = 1234
)
```

### Arguments

 `y1` Array with the observations of the first time series of the BIAR process. `y2` Array with the observations of the second time series of the BIAR process. `t` Array with the irregular observational times. `delta1` Array with the measurements error standard deviations of the first time series of the BIAR process. `delta2` Array with the measurements error standard deviations of the second time series of the BIAR process. `zero.mean` logical; if true, the array y has zero mean; if false, y has a mean different from zero. `niter` Number of iterations in which the function nlminb will be repeated. `seed` a single value, interpreted as the seed of the random process.

### Value

A list with the following components:

• phiR MLE of the autocorrelation coefficient of BIAR model (phiR).

• phiI MLE of the cross-correlation coefficient of the BIAR model (phiI).

• ll Value of the negative log likelihood evaluated in phiR and phiI.

### References

\insertRef

Elorrieta_2021iAR

`gentime`, `BIARsample`, `BIARphikalman`

### Examples

```
n=80
set.seed(6714)
st<-gentime(n)
x=BIARsample(n=n,phiR=0.9,phiI=0,st=st,rho=0)
y=x\$y
y1=y/apply(y,1,sd)
biar=BIARkalman(y1=y1[1,],y2=y1[2,],t=st,delta1 = rep(0,length(y[1,])),
delta2=rep(0,length(y[1,])))
biar

```

iAR documentation built on Nov. 25, 2022, 1:06 a.m.