Package | Description |
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de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise |
Normalizations operating on columns / variates; where each column is treated independently.
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de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator |
Estimators for statistical distributions.
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de.lmu.ifi.dbs.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.
|
Modifier and Type | Field and Description |
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private List<DistributionEstimator<?>> |
AttributeWiseCDFNormalization.estimators
Stores the distribution estimators
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private List<DistributionEstimator<?>> |
AttributeWiseCDFNormalization.Parameterizer.estimators
Stores the distribution estimators
|
private List<DistributionEstimator<?>> |
AttributeWiseBetaNormalization.estimators
Stores the distribution estimators
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private List<DistributionEstimator<?>> |
AttributeWiseBetaNormalization.Parameterizer.estimators
Stores the distribution estimators
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Constructor and Description |
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AttributeWiseBetaNormalization(List<DistributionEstimator<?>> estimators,
double alpha)
Constructor.
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AttributeWiseCDFNormalization(List<DistributionEstimator<?>> estimators)
Constructor.
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Modifier and Type | Interface and Description |
---|---|
interface |
ExpMADDistributionEstimator<D extends Distribution>
Distribuition estimators that use the method of moments (MOM) in
exponentiated data.
|
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 |
LogMOMDistributionEstimator<D extends Distribution>
Distribuition 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>
Distribuition estimators that use the method of moments (MOM), i.e. that only
need the statistical moments of a data set.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractExpMADEstimator<D extends Distribution>
Abstract base class for estimators based on the median and MAD.
|
class |
AbstractLMMEstimator<D extends Distribution>
Abstract base class for L-Moments based estimators (LMM).
|
class |
AbstractLogMADEstimator<D extends Distribution>
Abstract base class for estimators based on the median and MAD.
|
class |
AbstractLogMeanVarianceEstimator<D extends Distribution>
Estimators that work on Mean and Variance only (i.e. the first two moments
only).
|
class |
AbstractLogMOMEstimator<D extends Distribution>
Abstract base class for estimators based on the statistical moments.
|
class |
AbstractMADEstimator<D extends Distribution>
Abstract base class for estimators based on the median and MAD.
|
class |
AbstractMeanVarianceEstimator<D extends Distribution>
Estimators that work on Mean and Variance only (i.e. the first two moments
only).
|
class |
AbstractMOMEstimator<D extends Distribution>
Abstract base class for estimators based on the statistical moments.
|
class |
CauchyMADEstimator
Estimate Cauchy distribution parameters using Median and MAD.
|
class |
EMGOlivierNorbergEstimator
Naive distribution estimation using mean and sample variance.
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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 |
GammaMADEstimator
Robust parameter estimation for the Gamma distribution.
|
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 |
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 |
LogGammaAlternateExpMADEstimator
Robust parameter estimation for the LogGamma distribution.
|
class |
LogGammaChoiWetteEstimator
Estimate distribution parameters using the method by Choi and Wette.
|
class |
LogGammaLogMADEstimator
Robust parameter estimation for the LogGamma distribution.
|
class |
LogGammaLogMOMEstimator
Simple parameter estimation for the Gamma 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 |
WaldMLEstimator
Estimate parameter of the Wald distribution.
|
class |
WaldMOMEstimator
Estimate parameter of the Wald distribution.
|
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).
|
class |
WeibullLogMOMEstimator
Naive parameter estimation via least squares.
|
Modifier and Type | Class and 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 |
WinsorisingEstimator<D extends Distribution>
Winsorising or Georgization estimator.
|
Modifier and Type | Field and Description |
---|---|
private DistributionEstimator<D> |
WinsorisingEstimator.inner
Distribution estimator to use.
|
private DistributionEstimator<D> |
WinsorisingEstimator.Parameterizer.inner
Distribution estimator to use.
|
private DistributionEstimator<D> |
TrimmedEstimator.inner
Distribution estimator to use.
|
private DistributionEstimator<D> |
TrimmedEstimator.Parameterizer.inner
Distribution estimator to use.
|
Constructor and Description |
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TrimmedEstimator(DistributionEstimator<D> inner,
double trim)
Constructor.
|
WinsorisingEstimator(DistributionEstimator<D> inner,
double winsorize)
Constructor.
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Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.