blr: Parent Distribution Estimation with B-Spline Linear Regression Estimator

Description

Given observations on several order statistics, this function use the B-Spline linear regression estimator (Chou and Tao, 2010) to estimate the corresponding parent distribution of these order statistics nonparametrically.

Usage

blr(dat, orderinfo, degree = 3, support = NULL, constraint = FALSE)

Arguments

dat

a list consisting of the vectors of observations on various order statistics.

orderinfo

a matrix about the ranks and the sizes of various order statistics.

degree

the degree of B-spline used for estimation. The default is 3, i.e. cubic B-spline.

support

a vector specifying the support of the parent distribution. If unknown, it can be omitted, and the interval of data will be used as the support.

constraint

whether add monotone constraints in estimation. The default is no (FALSE).

Value

blr gives a list consisting of two element: betahat and n.knots. These two elements will be used in parentest for estimation of parent cdf.

Details

The details about specifying the above arguments can be found in bgmm.

Examples

Run this code

n.order<-c(20, 20, 60)	# number of observations for each order statistic below.
m<-5	# the size of random samples is 5.
# The three order statistics are 1:5 (the minimum), 3:5 (the sample median), 
# and 5:5 (the maximum)
rank.x<-c(1, 3, 5)
data.example<-list()
for(i in 1:3){
sorted.sample<-t(apply(matrix(rnorm(m*n.order[i]),nr=n.order[i],nc=m), 1, sort))
data.example[[i]]<-sorted.sample[,rank.x[i]]
}
order.example<-rbind(c(1, 5), c(3, 5), c(5, 5), deparse.level=0)
blr.example<-blr(data.example, order.example)