A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness-of-fit. Let x1,...,xm and y1,...,yn be two independent samples from distributions F and G that satisfy
mele.theta.p returns rank-based maximum likelihood estimates of θ,
probability masses p1,...,pN of F at the sorted pooled sample values
|License:||GPL 2.0 or newer|
The most important function is
mrle.sporm which returns the maximum rank-based likelihood
estimates the proportionality paramter θ and the baseline distribution.
ks.sporm is used to do the GOF test of the model assumption using a Kolmogorov-Smirnov type
confid.int.theta returns a confidence interval for θ;
test.theta does the hypothesis testing for θ;
calculates the profile loglikelihood l(θ) on interval
(θ1,θ2) which contains θ-hat; and
plot the empirical odds ratio. Functions
phi can be used to calculate other initials. There are few internal functions:
RadarTube contains the failure times (in days) of two types of radar tubes.
Zhong Guan <firstname.lastname@example.org>; Cheng Peng <email@example.com>
Zhong Guan <firstname.lastname@example.org>
Zhong Guan and Cheng Peng (2011), "A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness-of-fit", Journal of Nonparametric Statistics, to appear.