NormalFactory¶
(Source code
, png
)
- class NormalFactory(*args)¶
Normal factory.
- Parameters:
- robustbool, optional
Flag to select robust estimators of the parameters.
By default, robust is False.
See also
Notes
The parameters are estimated by likelihood maximization if robust=False:
If robust=True, the estimation is done using the empirical median as an estimate of , the empirical inter-quartile as an estimate of the standard deviation, where and are the 75% and 25% quantiles of the standard normal distribution, and the correlation matrix is estimated as the shape matrix of the underlying
NormalCopula
usingNormalCopulaFactory
.Examples
In the following example, the parameters of a
Normal
are estimated from a sample.>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> size = 10000 >>> distribution = ot.Normal(1.0, 2.0) >>> sample = distribution.getSample(size) >>> factory = ot.NormalFactory() >>> estimated = factory.build(sample)
Methods
build
(*args)Build the distribution.
buildAsNormal
(*args)Estimate the distribution as native distribution.
buildEstimator
(sample)Build the distribution and the parameter distribution.
Accessor to the bootstrap size.
Accessor to the object's name.
getName
()Accessor to the object's name.
hasName
()Test if the object is named.
setBootstrapSize
(bootstrapSize)Accessor to the bootstrap size.
setName
(name)Accessor to the object's name.
- __init__(*args)¶
- build(*args)¶
Build the distribution.
Available usages:
build()
build(sample)
build(param)
- Parameters:
- sample2-d sequence of float
Data.
- paramsequence of float
The parameters of the distribution.
- Returns:
- dist
Distribution
The estimated distribution.
In the first usage, the default native distribution is built.
- dist
- buildAsNormal(*args)¶
Estimate the distribution as native distribution.
Available usages:
buildAsNormal()
buildAsNormal(sample)
buildAsNormal(param)
- buildEstimator(sample)¶
Build the distribution and the parameter distribution.
- Parameters:
- sample2-d sequence of float
Data.
- parameters
DistributionParameters
Optional, the parametrization.
- Returns:
- resDist
DistributionFactoryResult
The results.
- resDist
Notes
According to the way the native parameters of the distribution are estimated, the parameters distribution differs:
Moments method: the asymptotic parameters distribution is normal and estimated by Bootstrap on the initial data;
Maximum likelihood method with a regular model: the asymptotic parameters distribution is normal and its covariance matrix is the inverse Fisher information matrix;
Other methods: the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting (see
KernelSmoothing
).
If another set of parameters is specified, the native parameters distribution is first estimated and the new distribution is determined from it:
if the native parameters distribution is normal and the transformation regular at the estimated parameters values: the asymptotic parameters distribution is normal and its covariance matrix determined from the inverse Fisher information matrix of the native parameters and the transformation;
in the other cases, the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting.
- getBootstrapSize()¶
Accessor to the bootstrap size.
- Returns:
- sizeint
Size of the bootstrap.
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- setBootstrapSize(bootstrapSize)¶
Accessor to the bootstrap size.
- Parameters:
- sizeint
The size of the bootstrap.
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
Examples using the class¶
Get the asymptotic distribution of the estimators
Use the Kolmogorov/Lilliefors test
Estimate Sobol indices on a field to point function