Description Details Author(s) References See Also Examples
Implements the maximum likelihood estimator (MLE) for a probability mass function (PMF) under the assumption of log-concavity from i.i.d. data.
Package: | logcondiscr |
Type: | Package |
Version: | 1.0.6 |
Date: | 2015-07-03 |
License: | GPL (>=2) |
LazyLoad: | yes |
The main functions in the package are:
logConDiscrMLE
: Compute the maximum likelihood estimator (MLE) of a log-concave PMF from i.i.d. data. The constrained log-likelihood function is maximized using an active set algorithm as initially described in Weyermann (2007).
logConDiscrCI
: Compute the maximum likelihood estimator (MLE) of a log-concave PMF from i.i.d. data and corresponding, asymptotically valid, pointwise confidence bands as
developed in Balabdaoui et al (2012).
kInflatedLogConDiscr
: Compute an estimate of a mixture of a log-concave PMF that is inflated at k, from i.i.d. data, using an EM algorithm.
Kaspar Rufibach (maintainer) kaspar.rufibach@gmail.com
http://www.kasparrufibach.ch
Fadoua Balabdaoui fadoua@ceremade.dauphine.fr
http://www.ceremade.dauphine.fr/~fadoua
Hanna Jankowski hkj@mathstat.yorku.ca
http://www.math.yorku.ca/~hkj
Kathrin Weyermann
Balabdaoui, F., Jankowski, H., Rufibach, K., and Pavlides, M. (2013). Maximum likelihood estimation and confidence bands for a discrete log-concave distribution. J. R. Stat. Soc. Ser. B Stat. Methodol., 75(4), 769–790.
Weyermann, K. (2007). An Active Set Algorithm for Log-Concave Discrete Distributions. MSc thesis, University of Bern (Supervisor: Lutz Duembgen).
Functions to estimate the log-concave MLE for a univariate continuous distribution are provided in the package logcondens and for observations in more than one dimension in LogConDEAD.
1 | ## see the help files for the abovementioned functions for examples
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