rp: Signed log-likelihood ratio statistic

In ProbYX: Inference for the Stress-Strength Model R = P(Y<X)

Signed log-likelihood ratio statistic

Description

Compute the signed log-likelihood ratio statistic (r_p) for a given value of the stress strength R = P(Y<X), that is the parameter of interest, under given parametric model assumptions.

Usage

rp(ydat, xdat, psi, distr = "exp")

Arguments

ydat	data vector of the sample measurements from Y.
xdat	data vector of the sample measurements from X.
psi	scalar for the parameter of interest. It is the value of R, treated as a parameter under the parametric model construction.
distr	character string specifying the type of distribution assumed for Y and X. Possible choices for distr are "exp" (default) for the one-parameter exponential, "norm_EV" and "norm_DV" for the Gaussian distribution with, respectively, equal or unequal variances assumed for the two random variables.

Details

The two independent random variables Y and X with given distribution distr are measurements of the diagnostic marker on the diseased and non-diseased subjects, respectively. For the relationship of the parameter of interest (R) and nuisance parameters with the original parameters of distr, look at the details in loglik.

Value

Value of the signed log-likelihood ratio statistic r_p.

Note

The r_p values can be also used for testing statistical hypotheses on the probability R.

Author(s)

Giuliana Cortese

References

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

Severini TA. (2000). Likelihood Methods in Statistics. Oxford University Press, New York.

Brazzale AR., Davison AC., Reid N. (2007). Applied Asymptotics. Case-Studies in Small Sample Statistics. Cambridge University Press, Cambridge.

See Also

wald, rpstar, MLEs, Prob

Examples

	# 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)      
	# value of \eqn{r_p} for \code{psi=0.9}
	rp(Y, X, 0.9,"norm_DV")             

ProbYX documentation built on June 21, 2022, 9:05 a.m.