Gives the Integrated Empirical Distribution Function

Description

Like intECDF, except returns a function \bar I instead of a value. The function \bar I(l,r) is given by

\bar{I}(l,r) = \int_{l}^{r} \bar{F}(u) du

where \bar F is the empirical distribution function of x_1,…,x_m. Note that l and r must lie in [x_1,x_m].

For an exact formula related to \bar{I}, see intECDF.

Usage

 1 intECDFfn(x) 

Arguments

 x Vector x = (x_1, …, x_m) of original observations, which are used to define the empirical CDF, \bar{F}.

Value

The function \bar I.

References

Duembgen, L, Huesler, A. and Rufibach, K. (2010) Active set and EM algorithms for log-concave densities based on complete and censored data. Technical report 61, IMSV, Univ. of Bern, available at http://arxiv.org/abs/0707.4643.

Duembgen, L. and Rufibach, K. (2009) Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency. Bernoulli, 15(1), 40–68.

Duembgen, L. and Rufibach, K. (2011) logcondens: Computations Related to Univariate Log-Concave Density Estimation. Journal of Statistical Software, 39(6), 1–28. http://www.jstatsoft.org/v39/i06

Doss, C. R. (2013). Shape-Constrained Inference for Concave-Transformed Densities and their Modes. PhD thesis, Department of Statistics, University of Washington, in preparation.

Doss, C. R. and Wellner, J. A. (2013). Inference for the mode of a log-concave density. Technical Report, University of Washington, in preparation.

See intECDF which returns values instead of a function.
 1 2 3 4 5 6 7 8 set.seed(100) xx <- runif(50) ## min .056, max .88 myIntECDF <- intECDFfn(xx); evalpts <- c(.3, .5) ## lie within [ min(xx) , max(xx) ] myIntECDF(evalpts) ## equal to intECDF(evalpts, xx)