code: function kde_estimator_silverman( vector ) {
  	var stdev, N = vector.length, A;
  
  	// [0] Calculate the sample standard deviation:
  	stdev = Vector.stdev( vector );
  
  	// [1] Calculate the estimator:
  	A = Math.pow( ( 4/(3*N) ), 0.2 );
  	return A * stdev;
  }
docs: end METHOD estimator() 
  FUNCTION: kde_estimator_silverman( vector )
  Use's Silverman's rule of thumb to derive an empirical estimate for an optimal KDE bandwidth selection.
  Source:
  Silverman, B.W. (1998). Density Estimation for Statistics and Data Analysis. London: Chapman & Hall/CRC. p. 48. ISBN 0-412-24620-1.
  
  @param {array} vector - 1d array over which to compute the estimate
  @returns {number} bandwidth estimate