Inference on the stress-strength model R = P(Y

Description

Compute confidence intervals and point estimates for R, under parametric model assumptions for Y and X. Y and X are two independent continuous random variables from two different populations.

Details

Package: ProbYX
Type: Package
Version: 1.1
Date: 2012-03-20
License: GPL-2
LazyLoad: yes

The package can be used for computing accurate confidence intervals and point estimates for the stress-strength (reliability) model R = P(Y<X); maximum likelihood estimates, Wald statistic, signed log-likelihood ratio statistic and its modified version ca be computed.
The main function is Prob, which evaluates confidence intervals and point estimates under different approaches and parametric assumptions.

Author(s)

Giuliana Cortese

Maintainer: Giuliana Cortese <gcortese@stat.unipd.it>

References

Cortese G., Ventura L. (2013). Accurate higher-order likelihood inference on P(Y<X). Computational Statistics, 28:1035-1059.

Kotz S, Lumelskii Y, Pensky M. (2003). The Stress-Strength Model and its Generalizations. Theory and Applications. World Scientific, Singapore.

Examples

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	 # data from the first population
	 Y <- rnorm(15, mean=5, sd=1)    
	 # data from the second population
	 X <- rnorm(10, mean=7, sd=1.5)      
     level <- 0.01        # \eqn{\alpha} level 
     # estimate and confidence interval under the assumption of two
     # normal variables with different variances.
	 Prob(Y, X, "norm_DV", "RPstar", level)   
	 # method has to be set equal to "RPstar".