Uses of Interface
elki.math.statistics.distribution.estimator.DistributionEstimator

Packages that use DistributionEstimator

Package	Description
elki.datasource.filter.normalization.columnwise	Normalizations operating on columns / variates; where each column is treated independently.
elki.math.statistics.distribution.estimator	Estimators for statistical distributions.
elki.math.statistics.distribution.estimator.meta	Meta estimators: estimators that do not actually estimate themselves, but instead use other estimators, e.g., on a trimmed data set, or as an ensemble.

Uses of DistributionEstimator in elki.datasource.filter.normalization.columnwise

Fields in elki.datasource.filter.normalization.columnwise with type parameters of type DistributionEstimator

Modifier and Type	Field	Description
private java.util.List<? extends DistributionEstimator<?>>	AttributeWiseBetaNormalization.Par.estimators	Stores the distribution estimators
protected java.util.List<? extends DistributionEstimator<?>>	AttributeWiseCDFNormalization.estimators	Stores the distribution estimators
private java.util.List<? extends DistributionEstimator<?>>	AttributeWiseCDFNormalization.Par.estimators	Stores the distribution estimators

Constructor parameters in elki.datasource.filter.normalization.columnwise with type arguments of type DistributionEstimator

Constructor	Description
AttributeWiseBetaNormalization(java.util.List<? extends DistributionEstimator<?>> estimators, double alpha)	Constructor.
AttributeWiseCDFNormalization(java.util.List<? extends DistributionEstimator<?>> estimators)	Constructor.

Uses of DistributionEstimator in elki.math.statistics.distribution.estimator

Subinterfaces of DistributionEstimator in elki.math.statistics.distribution.estimator

Modifier and Type	Interface	Description
interface	LMMDistributionEstimator<D extends Distribution>	Interface for distribution estimators based on the methods of L-Moments (LMM).
interface	LogMADDistributionEstimator<D extends Distribution>	Distribuition estimators that use the method of moments (MOM) in logspace.
interface	LogMeanVarianceEstimator<D extends Distribution>	Estimators that work on Mean and Variance only (i.e. the first two moments only).
interface	LogMOMDistributionEstimator<D extends Distribution>	Distribution estimators that use the method of moments (MOM) in logspace, i.e. that only need the statistical moments of a data set after logarithms.
interface	MADDistributionEstimator<D extends Distribution>	Distribuition estimators that use the method of moments (MOM), i.e. that only need the statistical moments of a data set.
interface	MeanVarianceDistributionEstimator<D extends Distribution>	Interface for estimators that only need mean and variance.
interface	MOMDistributionEstimator<D extends Distribution>	Distribution estimators that use the method of moments (MOM), i.e. that only need the statistical moments of a data set.

Classes in elki.math.statistics.distribution.estimator that implement DistributionEstimator

Modifier and Type	Class	Description
class	CauchyMADEstimator	Estimate Cauchy distribution parameters using Median and MAD.
class	EMGOlivierNorbergEstimator	Naive distribution estimation using mean and sample variance.
class	ExpGammaExpMOMEstimator	Simple parameter estimation for the ExpGamma distribution.
class	ExponentialLMMEstimator	Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).
class	ExponentialMADEstimator	Estimate Exponential distribution parameters using Median and MAD.
class	ExponentialMedianEstimator	Estimate Exponential distribution parameters using Median and MAD.
class	ExponentialMOMEstimator	Estimate Exponential distribution parameters using the mean, which is the maximum-likelihood estimate (MLE), but not very robust.
class	GammaChoiWetteEstimator	Estimate distribution parameters using the method by Choi and Wette.
class	GammaLMMEstimator	Estimate the parameters of a Gamma Distribution, using the methods of L-Moments (LMM).
class	GammaMOMEstimator	Simple parameter estimation for the Gamma distribution.
class	GeneralizedExtremeValueLMMEstimator	Estimate the parameters of a Generalized Extreme Value Distribution, using the methods of L-Moments (LMM).
class	GeneralizedLogisticAlternateLMMEstimator	Estimate the parameters of a Generalized Logistic Distribution, using the methods of L-Moments (LMM).
class	GeneralizedParetoLMMEstimator	Estimate the parameters of a Generalized Pareto Distribution (GPD), using the methods of L-Moments (LMM).
class	GumbelLMMEstimator	Estimate the parameters of a Gumbel Distribution, using the methods of L-Moments (LMM).
class	GumbelMADEstimator	Parameter estimation via median and median absolute deviation from median (MAD).
class	InverseGaussianMLEstimator	Estimate parameter of the inverse Gaussian (Wald) distribution.
class	InverseGaussianMOMEstimator	Estimate parameter of the inverse Gaussian (Wald) distribution.
class	LaplaceLMMEstimator	Estimate Laplace distribution parameters using the method of L-Moments (LMM).
class	LaplaceMADEstimator	Estimate Laplace distribution parameters using Median and MAD.
class	LaplaceMLEEstimator	Estimate Laplace distribution parameters using Median and mean deviation from median.
class	LogGammaLogMOMEstimator	Simple parameter estimation for the LogGamma distribution.
class	LogisticLMMEstimator	Estimate the parameters of a Logistic Distribution, using the methods of L-Moments (LMM).
class	LogisticMADEstimator	Estimate Logistic distribution parameters using Median and MAD.
class	LogLogisticMADEstimator	Estimate Logistic distribution parameters using Median and MAD.
class	LogNormalBilkovaLMMEstimator	Alternate estimate the parameters of a log Gamma Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.
class	LogNormalLevenbergMarquardtKDEEstimator	Distribution parameter estimation using Levenberg-Marquardt iterative optimization and a kernel density estimation.
class	LogNormalLMMEstimator	Estimate the parameters of a log Normal Distribution, using the methods of L-Moments (LMM) for the Generalized Normal Distribution.
class	LogNormalLogMADEstimator	Estimator using Medians.
class	LogNormalLogMOMEstimator	Naive distribution estimation using mean and sample variance.
class	NormalLevenbergMarquardtKDEEstimator	Distribution parameter estimation using Levenberg-Marquardt iterative optimization and a kernel density estimation.
class	NormalLMMEstimator	Estimate the parameters of a normal distribution using the method of L-Moments (LMM).
class	NormalMADEstimator	Estimator using Medians.
class	NormalMOMEstimator	Naive maximum-likelihood estimations for the normal distribution using mean and sample variance.
class	RayleighLMMEstimator	Estimate the scale parameter of a (non-shifted) RayleighDistribution using the method of L-Moments (LMM).
class	RayleighMADEstimator	Estimate the parameters of a RayleighDistribution using the MAD.
class	RayleighMLEEstimator	Estimate the scale parameter of a (non-shifted) RayleighDistribution using a maximum likelihood estimate.
class	SkewGNormalLMMEstimator	Estimate the parameters of a skew Normal Distribution (Hoskin's Generalized Normal Distribution), using the methods of L-Moments (LMM).
class	UniformEnhancedMinMaxEstimator	Slightly improved estimation, that takes sample size into account and enhances the interval appropriately.
class	UniformLMMEstimator	Estimate the parameters of a normal distribution using the method of L-Moments (LMM).
class	UniformMADEstimator	Estimate Uniform distribution parameters using Median and MAD.
class	UniformMinMaxEstimator	Estimate the uniform distribution by computing min and max.
class	WeibullLMMEstimator	Estimate parameters of the Weibull distribution using the method of L-Moments (LMM).
class	WeibullLogMADEstimator	Parameter estimation via median and median absolute deviation from median (MAD).

Uses of DistributionEstimator in elki.math.statistics.distribution.estimator.meta

Classes in elki.math.statistics.distribution.estimator.meta that implement DistributionEstimator

Modifier and Type	Class	Description
class	BestFitEstimator	A meta estimator that will try a number of (inexpensive) estimations, then choose whichever works best.
class	TrimmedEstimator<D extends Distribution>	Trimmed wrapper around other estimators.
class	WinsorizingEstimator<D extends Distribution>	Winsorizing or Georgization estimator.

Fields in elki.math.statistics.distribution.estimator.meta declared as DistributionEstimator

Modifier and Type	Field	Description
(package private) DistributionEstimator<?>	BestFitEstimator.BestFit.est	Best estimator.
private DistributionEstimator<D>	TrimmedEstimator.inner	Distribution estimator to use.
private DistributionEstimator<D>	TrimmedEstimator.Par.inner	Distribution estimator to use.
private DistributionEstimator<D>	WinsorizingEstimator.inner	Distribution estimator to use.
private DistributionEstimator<D>	WinsorizingEstimator.Par.inner	Distribution estimator to use.

Methods in elki.math.statistics.distribution.estimator.meta with parameters of type DistributionEstimator

Modifier and Type	Method	Description
void	BestFitEstimator.BestFit.test(DistributionEstimator<?> est, Distribution d)	Test the goodness of fit, keep the best.
private void	BestFitEstimator.warnIfDebugging(java.lang.ArithmeticException e, DistributionEstimator<?> est)	Warn on arithmetic errors, if debug logging is enabled

Constructors in elki.math.statistics.distribution.estimator.meta with parameters of type DistributionEstimator

Constructor	Description
TrimmedEstimator(DistributionEstimator<D> inner, double trim)	Constructor.
WinsorizingEstimator(DistributionEstimator<D> inner, double winsorize)	Constructor.