IdtSngNDRE-class {MAINT.Data}

Class IdtSngNDRE

Description

Contains the results of a single class robust estimation for the Normal distribution, with the four different possible variance-covariance configurations.

Slots

RobNmuE:

Matrix with the maximum likelihood mean vectors estimates

CovConfCases:

List of the considered configurations

ModelNames:

Inherited from class IdtE. The model acronym formed by a "N", indicating a Normal model, followed by the configuration (Case 1 through Case 4)

ModelType:

Inherited from class IdtE. Indicates the model; always set to "Normal" in objects of the IdtSngNDRE class

ModelConfig:

Inherited from class IdtE. Configuration of the variance-covariance matrix: Case 1 through Case 4

NIVar:

Inherited from class IdtE. Number of interval variables

SelCrit:

Inherited from class IdtE. The model selection criterion; currently, AIC and BIC are implemented

logLiks:

Inherited from class IdtE. The logarithms of the likelihood function for the different cases

AICs:

Inherited from class IdtE. Value of the AIC criterion

BICs:

Inherited from class IdtE. Value of the BIC criterion

BestModel:

Inherited from class IdtE. Bestmodel indicates the best model according to the chosen selection criterion

SngD:

Inherited from class IdtE. Boolean flag indicating whether a single or a mixture of distribution were estimated. Always set to TRUE in objects of class IdtSngNDRE

rawSet

A vector with the trimmed subset elements used to compute the raw (not reweighted) MCD covariance estimate for the chosen configuration.

RewghtdSet

A vector with the final trimmed subset elements used to compute the tle estimates.

RobMD2

A vector with the robust squared Mahalanobis distances used to select the trimmed subset.

cnp2

A vector of length two containing the consistency correction factor and the finite sample correction factor of the final estimate of the covariance matrix.

raw.cov

A matrix with the raw MCD estimator used to compute the robust squared Mahalanobis distances of RobMD2.

raw.cnp2

A vector of length two containing the consistency correction factor and the finite sample correction factor of the raw estimate of the covariance matrix.

PerfSt

A a list with the following components:
RepSteps: A list with one component by Covariance Configuration, containing a vector with the number of refinement steps performed by the fasttle algorithm by replication.
RepLogLik: A list with one component by Covariance Configuration, containing a vector with the best log-likelihood found be fasttle algorithm by replication.
StpLogLik: A list with one component by Covariance Configuration, containing a matrix with the evolution of the log-likelihoods found be fasttle algorithm by replication and refinement step.

Extends

Class IdtSngDE, directly. Class IdtE, by class IdtSngDE, distance 2.

Methods

No methods defined with class IdtSngNDRE in the signature.

Author(s)

Pedro Duarte Silva <[e-mail]>
Paula Brito <mpbrito.fep.up.pt>

References

Brito, P., Duarte Silva, A. P. (2012), Modelling Interval Data with Normal and Skew-Normal Distributions. Journal of Applied Statistics 39(1), 3–20.

Duarte Silva, A.P., Filzmoser, P. and Brito, P. (2017), Outlier detection in interval data. Advances in Data Analysis and Classification, 1–38.

See Also

IData, fasttle, fulltle, IdtSngNDE, IdtMxNDRE