A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

W

W(int) - Method in class com.numericalmethod.suanshu.stats.dlm.StateEquation

Get W(t), the covariance matrix of w_t.

wA - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the weighted design matrix, w

Weibull - Class in com.numericalmethod.suanshu.stats.random.distribution

Sample pseudo random numbers from the WeibullDistribution distribution.

Weibull(double, double, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.Weibull

Construct a pseudo-random number generator of a WeibullDistribution distribution.

Weibull() - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.Weibull

Construct a pseudo random number generator of the standard WeibullDistribution distribution.

WeibullDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate

The WeibullDistribution distribution interpolates between the exponential distribution (k = 1) and the Rayleigh distribution (k = 2), where k is the shape parameter.

WeibullDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution

Construct a WeibullDistribution distribution.

weights() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting

Get the weights to the observations.

weights() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS

weights - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the weights to each observation

wFitted - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals

the weighted, fitted values

which(double[], R.which) - Static method in class com.numericalmethod.suanshu.misc.R

Get the indices of the array elements which satisfy the boolean criterion.

which(int[], R.which) - Static method in class com.numericalmethod.suanshu.misc.R

Get the indices of the array elements which satisfy the boolean criterion.

White - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity

The White test is used to test for heteroskedasticity in a linear regression model.

White(Residuals) - Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White

Perform the White test to test for heteroskedasticity in a linear regression model.

WilcoxonRankSum - Class in com.numericalmethod.suanshu.stats.test.rank.wilcoxon

The Wilcoxon rank sum test tests for the equality of means of two population, or whether the means differs by an offset.

WilcoxonRankSum(double[], double[], double, boolean, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two population, or whether the means differs by an offset.

WilcoxonRankSum(double[], double[], double, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two population, or whether the means differs by an offset.

WilcoxonRankSum(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two population, or whether the means differs by an offset.

WilcoxonRankSum(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two population.

WilcoxonRankSumDistribution - Class in com.numericalmethod.suanshu.stats.test.rank.wilcoxon

Compute the exact distribution of the Wilcoxon rank sum test statistic.

WilcoxonRankSumDistribution(int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution

Construct a Wilcoxon Rank Sum distribution for sample sizes M and N.

WilcoxonSignedRank - Class in com.numericalmethod.suanshu.stats.test.rank.wilcoxon

The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and for the two-sample case, the equality of median of groups.

WilcoxonSignedRank(double[], double[], double, boolean) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[], int) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRankDistribution - Class in com.numericalmethod.suanshu.stats.test.rank.wilcoxon

Compute exactly the distribution of the Wilcoxon signed rank test statistic.

WilcoxonSignedRankDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Construct a Wilcoxon Signed Rank distribution for a sample size N.

wResiduals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals

the weighted residuals

Wt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt

Get the current value(s) of the driving Brownian motion(s).

Wt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt

Get the current value(s) of the driving Brownian motion(s).

wy - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the weighted response vector