A Quick Demo of the James-Stein Estimator

Kyle Cranmer, June 25, 2015

The Problem

Consider a standard multivariate Gaussian distribution for $\vec x$ in $n$ dimensions centered around $\vec\mu$

\begin{equation} f(\vec{x}|\vec{\mu}) = \prod_{i=1}^{n} \frac{1}{\sqrt{2\pi}} \exp\left(-\frac{(x_i- \mu_i)^2}{2}\right)\;. \end{equation}

We want to compare the mean squared error \begin{equation} E[||\bar{\vec x}-\vec\mu||^2]) \end{equation}

of the sample mean (an unbiased estimator) \begin{equation} \overline xi = \frac{1}{m} \sum{j=1}^m x_{ij} \end{equation}

to the James-Stein estimator \begin{equation} x_{JS} = \left( 1 - \frac{n-2}{||\bar{x}||^2} \right) \bar{x} \end{equation}

(where $i$ is the component of the vector and $j$ is an index over the samples).

In [1]:
%pylab inline

Populating the interactive namespace from numpy and matplotlib
In [2]:
def jamesSteinEstimator(x):
    return x*(1. - (x.size-2.)/sum(x*x))
In [3]:
def RMS(x,mean):
    return sqrt(sum((x-mean)*(x-mean)))
In [4]:
def doRun(nDim,nSamp=1000):
    mean=zeros(nDim)
    data = zeros(nDim*nSamp).reshape(nDim,nSamp)
    for i in range(nDim):
        mean[i] = 0.5 #set value of the mean's components here 
        data[i,:] = random.normal(mean[i],1,nSamp).T
    data=data.T

    avRMS_js = 0
    avRMS_mle = 0
    for x in data:
        avRMS_js += RMS(jamesSteinEstimator(x),mean)
        avRMS_mle += RMS(x,mean)
    avRMS_js /= nSamp
    avRMS_mle /= nSamp
    return (avRMS_js, avRMS_mle)
In [5]:
nDimToTest = linspace(2,20)
av_js = zeros(nDimToTest.size)
av_mle = zeros(nDimToTest.size)
for i,nDim in enumerate(nDimToTest):
    [av_js[i], av_mle[i]] = doRun(int(nDim))
In [6]:
plt.plot(nDimToTest,av_js,c='b') #James Stein in blue
plt.plot(nDimToTest,av_mle,c='r') #MLE in red
plt.ylabel('Mean Squared Error')
plt.xlabel('Dimensionality')
Out[6]:
<matplotlib.text.Text at 0x10dd2b650>
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