JMSLTM Numerical Library 5.0.1

Package com.imsl.stat

Interface Summary
CdfFunction Public interface for the user-supplied cumulative distribution function to be used by InverseCdf and ChiSquaredTest.
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.
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
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.
ARAutoUnivariate.Formatter Simple formatter for ARAutoUnivariate logging
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.
ARSeasonalFit Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time 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, probability density functions, and their inverses.
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 and the use of either reclassification, split sample, or the leaving-out-one methods in order to evaluate the rule.
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.
GARCH Computes estimates of the parameters of a GARCH(p,q) model.
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.
KolmogorovOneSample The class KolmogorovOneSample performs a Kolmogorov-Smirnov goodness-of-fit test in one sample.
KolmogorovTwoSample Performs a Kolmogorov-Smirnov two-sample test.
LinearRegression Fits a multiple linear regression model with or without an intercept.
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.
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.
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 y = c_0  + c_1 f_1 (x) + c_2 f_2 (x) +  cdots  + c_k f_k (x) + varepsilon, where f_1 (x),f_2 (x), cdots ,f_k (x) are the user basis functions f_i (x) evaluated at index values i = 1,2, ldots ,k,c_0 is the intercept, c_1 ,c_2 , cdots ,c_k 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 The input triangular matrix is singular.
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.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.
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.
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.
ClusterKMeans.NonnegativeFreqException Frequencies must be nonnegative.
ClusterKMeans.NonnegativeWeightException Weights must be nonnegative.
Covariances.DiffObsDeletedException Different observations are being deleted from return matrix than were originally entered.
Covariances.MoreObsDelThanEnteredException More observations are being deleted from the output covariance matrix than were originally entered (the corresponding row, column of the incidence matrix is less than zero).
Covariances.NonnegativeFreqException Frequencies must be nonnegative.
Covariances.NonnegativeWeightException Weights must be nonnegative.
Covariances.TooManyObsDeletedException More observations have been deleted than were originally entered (the sum of frequencies has become negative).
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.NoDegreesOfFreedomException No degrees of freedom error.
FactorAnalysis.NonPositiveEigenvalueException Non positive eigenvalue error.
FactorAnalysis.NotPositiveDefiniteException Covariance matrix not positive definite.
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.TooManyIterationsException The number of iterations has exceeded the maximum allowed.
NormalityTest.NoVariationInputException There is no variation in the input data.
SelectionRegression.NoVariablesException No Variables can enter the model.
StepwiseRegression.CyclingIsOccurringException Cycling is occurring.
StepwiseRegression.NoVariablesEnteredException No Variables can enter the model.
 


JMSLTM Numerical Library 5.0.1

Copyright © 1970-2008 Visual Numerics, Inc.
Built July 8 2008.