Class Jackknife


public class Jackknife
extends Object
Applies the jackknife to generic statistics.

This class applies the jackknife method (see, e.g., “A leisurely look at the bootstrap, the jackknife, and cross-validation”, by Bradley Efron and Gail Gong, The American Statistician, 37(1):36−48, 1983) to reduce the bias in the estimation of a nonlinear statistic of interest (linear statistics, such as the mean, pass through the jackknife without change). The statistic must take a sample (an array of big decimals) and return corresponding values (again as an array of big decimals). In case high-precision arithmetic is not required, an instance of Jackknife.AbstractStatistic just takes an array of doubles and returns an array of doubles, handling all necessary type conversions.

The static method compute(List, Statistic, MathContext) takes a list of samples (arrays of doubles of the same length) and returns an instance of this class containing estimates and standard errors for every value computed by the statistic (estimates of the statistic are available both as an array of big decimals and as an array of doubles, whereas estimates of standard errors are provided in double format, only).

All computations are performed internally using BigDecimal and a provided MathContext. The method compute(List, Statistic) uses 100 decimal digits.

The identical statistic can be used to compute the (pointwise) empirical mean and standard error of a sample.

Sebastiano Vigna