# aryule

Autoregressive all-pole model parameters — Yule-Walker method

## Syntax

`a = aryule(x,p)[a,e] = aryule(x,p)[a,e,rc] = aryule(x,p)`

## Description

`a = aryule(x,p)` returns the normalized autoregressive (AR) parameters corresponding to a model of order `p` for the input array, `x`. If `x` is a vector, then the output array, `a`, is a row vector. If `x` is a matrix, then the parameters along the nth row of `a` model the nth column of `x`. `a` has `p` + 1 columns. `p` must be less than the number of elements (or rows) of `x`.

`[a,e] = aryule(x,p)` returns the estimated variance, `e`, of the white noise input.

`[a,e,rc] = aryule(x,p)` returns the reflection coefficients in `rc`.

## Examples

collapse all

### Parameter Estimation Using the Yule-Walker Method

Use a vector of polynomial coefficients to generate an AR(4) process by filtering 1024 samples of white noise. Reset the random number generator for reproducible results. Use the Yule-Walker method to estimate the coefficients.

```rng default A = [1 -2.7607 3.8106 -2.6535 0.9238]; y = filter(1,A,0.2*randn(1024,1)); arcoeffs = aryule(y,4) ```
```arcoeffs = 1.0000 -2.7262 3.7296 -2.5753 0.8927 ```

Generate 50 realizations of the process, changing each time the variance of the input noise. Compare the Yule-Walker-estimated variances to the actual values.

```nrealiz = 50; noisestdz = rand(1,nrealiz)+0.5; randnoise = randn(1024,nrealiz); for k = 1:nrealiz y = filter(1,A,noisestdz(k) * randnoise(:,k)); [arcoeffs,noisevar(k)] = aryule(y,4); end plot(noisestdz.^2,noisevar,'*') title('Noise Variance') xlabel('Input') ylabel('Estimated') ```

Repeat the procedure using `aryule`'s multichannel syntax.

```realiz = bsxfun(@times,noisestdz,randnoise); Y = filter(1,A,realiz); [coeffs,variances] = aryule(Y,4); hold on plot(noisestdz.^2,variances,'o') q = legend('Single channel loop','Multichannel'); q.Location = 'best'; ```

### Estimate Model order Using Decay of Reflection Coefficients

Use a vector of polynomial coefficients to generate an AR(2) process by filtering 1024 samples of white noise. Reset the random number generator for reproducible results.

```rng default y = filter(1,[1 -0.75 0.5],0.2*randn(1024,1)); ```

Use the Yule-Walker method to fit an AR(10) model to the process. Output and plot the reflection coefficients.

```[ar_coeffs,NoiseVariance,reflect_coeffs] = aryule(y,10); stem(reflect_coeffs) axis([-0.05 10.5 -1 1]) title('Reflection Coefficients by Lag') ```

The reflection coefficients decay to zero after lag 2, which indicates that an AR(10) model significantly overestimates the time dependence in the data.

expand all

### AR(p) Model

In an AR model of order p, the current output is a linear combination of the past p outputs plus a white noise input. The weights on the p past outputs minimize the mean-square prediction error of the autoregression. If y(n) is the current value of the output and x(n) is a zero-mean white noise input, the AR(p) model is:

$\sum _{k=0}^{p}a\left(k\right)y\left(n-k\right)=x\left(n\right).$

### Reflection Coefficients

The reflection coefficients are the partial autocorrelation coefficients scaled by –1. The reflection coefficients indicate the time dependence between y(n) and y(n – k) after subtracting the prediction based on the intervening k – 1 time steps.

### Algorithms

`aryule` uses the Levinson-Durbin recursion on the biased estimate of the sample autocorrelation sequence to compute the parameters.

## References

[1] Hayes, Monson H. Statistical Digital Signal Processing and Modeling. New York: John Wiley & Sons, 1996.