Package com.imsl.stat

Interface Summary

CdfFunction	Public interface for the user-supplied cumulative distribution function to be used by InverseCdf and ChiSquaredTest.
Distribution	Public interface for the user-supplied distribution function.
NonlinearRegression.Derivative	Public interface for the user supplied function to compute the derivative for NonlinearRegression.
NonlinearRegression.Function	Public interface for the user supplied function for NonlinearRegression.
ProbabilityDistribution	Public interface for a user-supplied probability distribution.
Random.BaseGenerator	Base pseudorandom number.
RandomSequence	Interface implemented by generators of random or quasi-random multidimension sequences.
RegressionBasis	Public interface for user supplied function to UserBasisRegression object.

Class Summary

ANCOVA	Analyzes a one-way classification model with covariates.
ANOVA	Analysis of Variance table and related statistics.
ANOVAFactorial	Analyzes a balanced factorial design with fixed effects.
ARAutoUnivariate	Automatically determines the best autoregressive time series model using Akaike's Information Criterion.
ARMA	Computes least-square estimates of parameters for an ARMA model.
ARMAEstimateMissing	Estimates missing values in a time series collected with equal spacing.
ARMAMaxLikelihood	Computes maximum likelihood estimates of parameters for an ARMA model with p and q autoregressive and moving average terms respectively.
ARMAOutlierIdentification	Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.
ARSeasonalFit	Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.
AutoARIMA	Automatically identifies time series outliers, determines parameters of a multiplicative seasonal model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.
AutoCorrelation	Computes the sample autocorrelation function of a stationary time series.
CategoricalGenLinModel	Analyzes categorical data using logistic, probit, Poisson, and other linear models.
Cdf	Cumulative probability distribution functions.
ChiSquaredTest	Chi-squared goodness-of-fit test.
ClusterHierarchical	Performs a hierarchical cluster analysis from a distance matrix.
ClusterKMeans	Perform a K-means (centroid) cluster analysis.
ContingencyTable	Performs a chi-squared analysis of a two-way contingency table.
Covariances	Computes the sample variance-covariance or correlation matrix.
CrossCorrelation	Computes the sample cross-correlation function of two stationary time series.
Difference	Differences a seasonal or nonseasonal time series.
DiscriminantAnalysis	Performs a linear or a quadratic discriminant function analysis among several known groups.
Dissimilarities	Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.
EmpiricalQuantiles	Computes empirical quantiles.
FactorAnalysis	Performs Principal Component Analysis or Factor Analysis on a covariance or correlation matrix.
FaureSequence	Generates the low-discrepancy Faure sequence.
GammaDistribution	Evaluates a gamma probability density for a given set of data.
GARCH	Computes estimates of the parameters of a GARCH(p,q) model.
InvCdf	Inverse cumulative probability distribution functions.
InverseCdf	Inverse of user-supplied cumulative distribution function.
KalmanFilter	Performs Kalman filtering and evaluates the likelihood function for the state-space model.
KaplanMeierECDF	Computes the Kaplan-Meier reliability function estimates or the CDF based on failure data that may be multi-censored.
KaplanMeierEstimates	Computes Kaplan-Meier (or product-limit) estimates of survival probabilities for a sample of failure times that possibly contain right consoring.
KolmogorovOneSample	The class KolmogorovOneSample performs a Kolmogorov-Smirnov goodness-of-fit test in one sample.
KolmogorovTwoSample	Performs a Kolmogorov-Smirnov two-sample test.
LackOfFit	Performs lack-of-fit test for a univariate time series or transfer function given the appropriate correlation function.
LifeTables	Computes population (current) or cohort life tables based upon the observed population sizes at the middle (for population table) or the beginning (for cohort table) of some user specified age intervals.
LinearRegression	Fits a multiple linear regression model with or without an intercept.
LogNormalDistribution	Evaluates a lognormal probability density for a given set of data.
MersenneTwister	A 32-bit Mersenne Twister generator.
MersenneTwister64	A 64-bit Mersenne Twister generator.
MultiCrossCorrelation	Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.
MultipleComparisons	Performs Student-Newman-Keuls multiple comparisons test.
NonlinearRegression	Fits a multivariate nonlinear regression model using least squares.
NormalDistribution	Evaluates the normal (Gaussian) probability density for a given set of data.
NormalityTest	Performs a test for normality.
NormOneSample	Computes statistics for mean and variance inferences using a sample from a normal population.
NormTwoSample	Computes statistics for mean and variance inferences using samples from two normal populations.
PartialCovariances	Class PartialCovariances computes the partial covariances or partial correlations from an input covariance or correlation matrix.
Pdf	Probability density functions.
PoissonDistribution	Evaluates a Poisson probability density of a given set of data.
ProportionalHazards	Analyzes survival and reliability data using Cox's proportional hazards model.
Random	Generate uniform and non-uniform random number distributions.
Ranks	Compute the ranks, normal scores, or exponential scores for a vector of observations.
RegressorsForGLM	Generates regressors for a general linear model.
SelectionRegression	Selects the best multiple linear regression models.
SignTest	Performs a sign test.
Sort	A collection of sorting functions.
StepwiseRegression	Builds multiple linear regression models using forward selection, backward selection, or stepwise selection.
Summary	Computes basic univariate statistics.
TableMultiWay	Tallies observations into a multi-way frequency table.
TableOneWay	Class TableOneWay calculates a frequency table for a data array.
TableTwoWay	Class TableTwoWay calculates a two-dimensional frequency table for a data array based upon two variables.
UserBasisRegression	Fits a linear function of the form , where are the user basis functions evaluated at index values is the intercept, are the coefficients associated with the basis functions, and is the random error associated with y.
WilcoxonRankSum	Performs a Wilcoxon rank sum test.

Exception Summary

ARAutoUnivariate.TriangularMatrixSingularException
ARMA.IllConditionedException	The problem is ill-conditioned.
ARMA.IncreaseErrRelException	The bound for the relative error is too small.
ARMA.MatrixSingularException	The input matrix is singular.
ARMA.NewInitialGuessException	The iteration has not made good progress.
ARMA.NoProgressException	The algorithm is not making any progress, try new initial guess.
ARMA.TooManyCallsException	The number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1.
ARMA.TooManyFcnEvalException	Maximum number of function evaluations exceeded.
ARMA.TooManyITNException	Maximum number of iterations exceeded.
ARMA.TooManyJacobianEvalException	Maximum number of Jacobian evaluations exceeded.
ARMAMaxLikelihood.InitialMAException	The initial values for the moving average parameters are not invertible.
ARMAMaxLikelihood.NonInvertibleException	The solution is noninvertible.
ARMAMaxLikelihood.NonStationaryException	The solution is nonstationary.
AutoARIMA.NoAcceptableModelFoundException	No appropriate ARIMA model could be found.
AutoCorrelation.NonPosVariancesException	The problem is ill-conditioned.
CategoricalGenLinModel.ClassificationVariableException	The ClassificationVariable vector has not been initialized.
CategoricalGenLinModel.ClassificationVariableLimitException	The Classification Variable limit set by the user through setUpperBound has been exceeded.
CategoricalGenLinModel.ClassificationVariableValueException	The number of distinct values for each Classification Variable must be greater than 1.
CategoricalGenLinModel.DeleteObservationsException	The number of observations to be deleted (set by setObservationMax) has grown too large.
CategoricalGenLinModel.RankDeficientException	The model has been determined to be rank deficient.
ChiSquaredTest.DidNotConvergeException	The iteration did not converge
ChiSquaredTest.NoObservationsException	There are no observations.
ChiSquaredTest.NotCDFException	The function is not a Cumulative Distribution Function (CDF).
ClusterKMeans.ClusterNoPointsException	There is a cluster with no points
ClusterKMeans.NoConvergenceException	Convergence did not occur within the maximum number of iterations.
Covariances.NonnegativeFreqException	Frequencies must be nonnegative.
Covariances.NonnegativeWeightException	Weights must be nonnegative.
CrossCorrelation.NonPosVariancesException	The problem is ill-conditioned.
DiscriminantAnalysis.CovarianceSingularException	The variance-covariance matrix is singular.
DiscriminantAnalysis.EmptyGroupException	There are no observations in a group.
DiscriminantAnalysis.SumOfWeightsNegException	The sum of the weights have become negative.
Dissimilarities.NoPositiveVarianceException	No variable has positive variance.
Dissimilarities.ScaleFactorZeroException	The computations cannot continue because a scale factor is zero.
Dissimilarities.ZeroNormException	The computations cannot continue because the Euclidean norm of the column is equal to zero.
EmpiricalQuantiles.ScaleFactorZeroException	The computations cannot continue because a scale factor is zero.
FactorAnalysis.BadVarianceException	Bad variance error.
FactorAnalysis.EigenvalueException	Eigenvalue error.
FactorAnalysis.NonPositiveEigenvalueException	Non positive eigenvalue error.
FactorAnalysis.NotPositiveSemiDefiniteException	Covariance matrix not positive semi-definite.
FactorAnalysis.NotSemiDefiniteException	Hessian matrix not semi-definite.
FactorAnalysis.RankException	Rank of covariance matrix error.
FactorAnalysis.SingularException	Covariance matrix singular error.
GARCH.ConstrInconsistentException	The equality constraints are inconsistent.
GARCH.EqConstrInconsistentException	The equality constraints and the bounds on the variables are found to be inconsistent.
GARCH.NoVectorXException	No vector X satisfies all of the constraints.
GARCH.TooManyIterationsException	Number of function evaluations exceeded 1000.
GARCH.VarsDeterminedException	The variables are determined by the equality constraints.
InverseCdf.DidNotConvergeException	The iteration did not converge
MultiCrossCorrelation.NonPosVariancesException	The problem is ill-conditioned.
NonlinearRegression.NegativeFreqException	A negative frequency was encountered.
NonlinearRegression.NegativeWeightException	A negative weight was encountered.
NonlinearRegression.NoProgressException	The algorithm is not making any progress, try new initial guess.
NonlinearRegression.TooManyIterationsException	The number of iterations has exceeded the maximum allowed.
NormalityTest.NoVariationInputException	There is no variation in the input data.
PartialCovariances.InvalidMatrixException	Exception thrown if a computed correlation is greater than one for some pair of variables.
PartialCovariances.InvalidPartialCorrelationException	Exception thrown if a computed partial correlation is greater than one for some pair of variables.
Pdf.AltSeriesAccuracyLossException	The magnitude of alternating series sum is too small relative to the sum of positive terms to permit a reliable accuracy.
ProportionalHazards.ClassificationVariableLimitException	The Classification Variable limit set by the user through setUpperBound has been exceeded.
SelectionRegression.NoVariablesException	No Variables can enter the model.
StepwiseRegression.CyclingIsOccurringException	Cycling is occurring.
StepwiseRegression.NoVariablesEnteredException	No Variables can enter the model.