org.akutan.utilities
Class Statistics

java.lang.Object
  org.akutan.utilities.Statistics

public class Statistics
extends java.lang.Object

Class that wraps the colt descriptive statistics class and provides a few extras along the same vein.

Since:

14 December 2007

Constructor Summary

Statistics()

Method Summary

static double	annualizeMean(double mean) Return the simple compounded annualization of the mean monthly return and convert from a decimal to a percent.
static double	annualizeStdDev(double sigma) Annualize the monthly standard deviation and convert from a decimal to a percent.
static double	beta(cern.colt.list.DoubleArrayList l1, cern.colt.list.DoubleArrayList l2) Called to compute the beta of series 1 with respect to series 2.
static double	chiSquared(double n, double x) Returns the probability density function for the chi squared distribution for n degrees of freedom at x.
static double	correlation(cern.colt.list.DoubleArrayList l1, cern.colt.list.DoubleArrayList l2) Compute the correlation of the series in the two lists
static double	erfinv(double y) Inverse error function from the Scilab source code
static double	kendallsTau(cern.colt.list.DoubleArrayList l1, cern.colt.list.DoubleArrayList l2) Compute kendall's tau for the two seriesthe sample covariance.
static double	makePercent(double decimal) Converts a decimal to a percent.
static double	mean(cern.colt.list.DoubleArrayList l) Compute the arithmetic mean of a list
static double	mean(int num, double sumx) Compute the mean from a sum of the values and the count.
static double	stddev(cern.colt.list.DoubleArrayList list) Compute the standard deviation using the code in colt
static double	stddev(int num, double sumx, double sumx2) Compute the standard deviation given the number of values, the sum of the values and the sum of the values squared.
static double	variance(int num, double sumx, double sumx2) Compute the variance of a sequence of values from the number of values, the sum of the values and the sum of the values squared.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Statistics

public Statistics()

Method Detail

erfinv

public static double erfinv(double y)

Inverse error function from the Scilab source code

Parameters:

y -

Returns:

The inverse error function for the value y

makePercent

public static double makePercent(double decimal)

Converts a decimal to a percent.

Parameters:

decimal - input

Returns:

Input value as a percent ( 0.20 = 20.0%)

annualizeMean

public static double annualizeMean(double mean)

Return the simple compounded annualization of the mean monthly return and convert from a decimal to a percent.

Parameters:

mean - Monthly mean return as a decimal

Returns:

Annualized (monthly compounding) mean returned as a percent

annualizeStdDev

public static double annualizeStdDev(double sigma)

Annualize the monthly standard deviation and convert from a decimal to a percent.

Parameters:

sigma - Monthly standard deviation as a decimal

Returns:

Annualized standard deviation returned as a percent

correlation

public static double correlation(cern.colt.list.DoubleArrayList l1,
                                 cern.colt.list.DoubleArrayList l2)

Compute the correlation of the series in the two lists

Parameters:

l1 - List of time series points

l2 - List of time series points

Returns:

covariance between the two lists

kendallsTau

public static double kendallsTau(cern.colt.list.DoubleArrayList l1,
                                 cern.colt.list.DoubleArrayList l2)

Compute kendall's tau for the two seriesthe sample covariance. Kendall's tau is a robust method to compute the correlation between two series in the presence of outliers. Given Kendall's tau, the correlation between the two series is p = sin(tau * pi/2).

Parameters:

l1 - List of time series points

l2 - List of time series points

Returns:

kendall's tau

Throws:

java.lang.IllegalArgumentException - if the lengths of the two series are different

beta

public static double beta(cern.colt.list.DoubleArrayList l1,
                          cern.colt.list.DoubleArrayList l2)

Called to compute the beta of series 1 with respect to series 2.

Parameters:

l1 - List of dependent series points

l2 - List of independent series points

Returns:

beta of dependent series to independent series

mean

public static double mean(cern.colt.list.DoubleArrayList l)

Compute the arithmetic mean of a list

Parameters:

l - List of time series points

Returns:

Arithmetic mean of points on the list

mean

public static double mean(int num,
                          double sumx)

Compute the mean from a sum of the values and the count.

Parameters:

num - Number of values

sumx - Sum of values

Returns:

Arithmetic mean of the series

stddev

public static double stddev(int num,
                            double sumx,
                            double sumx2)

Compute the standard deviation given the number of values, the sum of the values and the sum of the values squared.

Parameters:

num - Number of values

sumx - Sum of values

sumx2 - Sum of squared values

Returns:

Standard deviation of the series

variance

public static double variance(int num,
                              double sumx,
                              double sumx2)

Compute the variance of a sequence of values from the number of values, the sum of the values and the sum of the values squared.

Parameters:

num - Number of values

sumx - Sum of values

sumx2 - Sum of squared values

Returns:

Variance of the series

stddev

public static double stddev(cern.colt.list.DoubleArrayList list)

Compute the standard deviation using the code in colt

Parameters:

list - List of values

Returns:

Standard deviation of the values on the list

chiSquared

public static double chiSquared(double n,
                                double x)

Returns the probability density function for the chi squared distribution for n degrees of freedom at x.

Parameters:

n - Number of degrees of freedom

x - Point on the distribution

Returns:

PDF for Chi squared distribution at x with n degrees of freedom