mle-methods: Methods for function mle in Package 'MAINT.Data'

In MAINT.Data: Model and Analyse Interval Data

Methods for function mle in Package ‘MAINT.Data’

Description

Performs maximum likelihood estimation for parametric models of interval data

Usage

## S4 method for signature 'IData'
mle(Sdt, Model="Normal", CovCase="AllC", SelCrit=c("BIC","AIC"), 
  k2max=1e6, OptCntrl=list(), ...)

Arguments

Sdt	An IData object representing interval-valued units.
Model	The joint distribution assumed for the MidPoint and LogRanges. Current alternatives are “Normal” for Gaussian distributions, “SNNormal” for Skew-Normal and “NrmandSKN” for both Gaussian and Skew-Normal distributions.
CovCase	Configuration of the variance-covariance matrix: The string “AllC” for all possible configurations (default), or a set of integers between 1 and 4.
SelCrit	The model selection criterion.
k2max	Maximal allowed l2-norm condition number for correlation matrices. Correlation matrices with condition number above k2max are considered to be numerically singular, leading to degenerate results.
OptCntrl	List of optional control parameters to be passed to the optimization routine. See the documentation of RepLOptim for a description of the available options.
...	Other named arguments.

References

Azzalini, A. and Dalla Valle, A. (1996), The multivariate skew-normal distribution. Biometrika 83(4), 715–726.

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

See Also

IData, RepLOptim

Examples

# Create an Interval-Data object containing the intervals of temperatures by quarter 
# for 899 Chinese meteorological stations.

ChinaT <- IData(ChinaTemp[1:8])

# Estimate parameters by maximum likelihood assuming a Gaussian distribution

ChinaE <- mle(ChinaT)
cat("China maximum likelhiood estimation results =\n")
print(ChinaE)
cat("Standard Errors of Estimators:\n")
print(stdEr(ChinaE))

MAINT.Data documentation built on April 4, 2023, 9:09 a.m.