1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
|
\name{intECDF}
\alias{intECDF}
\title{Computes the Integrated Empirical Distribution Function at Arbitrary Real Numbers in s}
\description{Computes the value of
\deqn{\bar{I}(t) = \int_{x_1}^t \bar{F}(r) d \, r}{\bar{I}(t) = \int_{x_1}^t \bar{F}(r) dr}
where \eqn{\bar F} is the empirical distribution function of \eqn{x_1,\ldots,x_m}, at all real numbers \eqn{t} in the
vector \eqn{\bold{s}}{s}. Note that \eqn{t} (so all elements in \eqn{\bold{s}}{s}) must lie in \eqn{[x_1,x_m]}.
The exact formula for \eqn{\bar I(t)} is
\deqn{\bar I(t) = \Big(\sum_{i=2}^{i_0}(x_i-x_{i-1})\frac{i-1}{n} \Big) + (t-x_{i_0})\frac{i_0-1}{n}}{\bar I(t) = (\sum_{i=2}^{i_0}(x_i-x_{i-1}) (i-1)/n) + (t-x_{i_0})(i_0-1)/n}
where \eqn{i_0 = \max_{i=1,\ldots,m} \{x_i \le t\}}{i_0 = \max_{i=1,\ldots, m}{x_i \le t}}.
}
\usage{intECDF(s, x)}
\arguments{
\item{s}{Vector of real numbers in \eqn{[x_1,x_m]} where \eqn{\bar{I}} should be evaluated at.}
\item{x}{Vector \eqn{{\bold{x}} = (x_1, \ldots, x_m)}{x = (x_1, \ldots, x_m)} of original observations.}
}
\value{Vector of the same length as \eqn{\bold{s}}{s}, containing the values of \eqn{\bar I} at the elements of \eqn{\bold{s}}{s}.}
\author{
Kaspar Rufibach, \email{kaspar.rufibach@gmail.com}, \cr \url{http://www.kasparrufibach.ch}
Lutz Duembgen, \email{duembgen@stat.unibe.ch}, \cr \url{https://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html}}
\seealso{
This function together with \code{\link{intF}} can be used to check the characterization of the log-concave density
estimator in terms of distribution functions, see Rufibach (2006) and Duembgen and Rufibach (2009).
}
\references{
Duembgen, L. and Rufibach, K. (2009)
Maximum likelihood estimation of a log--concave density and its distribution function: basic properties and uniform consistency.
\emph{Bernoulli}, \bold{15(1)}, 40--68.
Duembgen, L. and Rufibach, K. (2011)
logcondens: Computations Related to Univariate Log-Concave Density Estimation.
\emph{Journal of Statistical Software}, \bold{39(6)}, 1--28. \doi{https://doi.org/10.18637/jss.v039.i06}
Rufibach K. (2006) \emph{Log-concave Density Estimation and Bump Hunting for i.i.d. Observations.}
PhD Thesis, University of Bern, Switzerland and Georg-August University of Goettingen, Germany, 2006.
\cr Available at \url{https://slsp-ube.primo.exlibrisgroup.com/permalink/41SLSP_UBE/17e6d97/alma99116730175505511}.
}
\examples{
# for an example see the function intF.
}
\keyword{htest}
\keyword{nonparametric}
|
