"IdtMxNDRE" contains the results of a mixture Normal model robust parameter estimation, with the four different possible variance-covariance configurations.
Hmcdt:Indicates whether we consider an homocedastic (TRUE) or a hetereocedasic model (FALSE)
RobNmuE:Matrix with the robust mean vectors estimates by group (each row refers to a group)
CovConfCases:List of the considered configurations
grouping:Inherited from class "IdtMxE". Factor indicating the group to which each observation belongs to
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 IdtMxNDRE class
ModelConfig:Inherited from class "IdtE". Configuration case 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". 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 FALSE in objects of class "IdtMxNDRE"
Ngrps:Inherited from class "IdtMxE". Number of mixture components
No methods defined with class "IdtMxNDRE" in the signature.
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.
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