Uses of Interface
elki.math.statistics.distribution.estimator.DistributionEstimator
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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. -
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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 estimatorsprotected java.util.List<? extends DistributionEstimator<?>>
AttributeWiseCDFNormalization. estimators
Stores the distribution estimatorsprivate java.util.List<? extends DistributionEstimator<?>>
AttributeWiseCDFNormalization.Par. estimators
Stores the distribution estimatorsConstructor 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 enabledConstructors 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.
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