IdtSngNDRE-class: Class "IdtSngNDRE"

Description

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

Arguments

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"

Extends

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

Methods

No methods defined with class "IdtSngNDRE" in the signature.

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.

Hadi, A. S. and Luceno, A. (1997), Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Computational Statistics and Data Analysis 25(3), 251--272.