Section 4 is devoted to applications of the smoothed logconcave maximum likelihood estimator to classi. If a density is logconcave, so is its cumulative distribution function cdf. Inference and modeling with logconcave distributions. Pdf a fast algorithm for univariate logconcave density. Logconcdead logconcave density estimation in arbitrary dimensions. Exact solutions in logconcave maximum likelihood estimation. Although first uploaded to cran in 2006, a detailed description beyond the package manual. Maximum likelihood estimation of a logconcave density has attracted considerable. If a density is log concave, so is its cumulative distribution function cdf. Cule, samworth, and stewart showed that the logarithm of the optimal logconcave density is. Maximum likelihood estimation of a logconcave density based.
Limit distribution theory for maximum likelihood estimation of a log concave density. Maximum likelihood estimation of a logconcave density and its distribution function. Under fairly general conditions, the logconcave maximum likelihood estimator lcmle exists and is consistent. In this paper, a much more flexible mixture model is considered, which assumes each component density to be logconcave. Nonparametric density estimation, shape constraint, logconcave density, polya frequency function, strongly unimodal, iterative convex minorant algorithm, active set algorithm. Mode estimation, logconcave density on r let x0 mf0 be the mode of the logconcave density f0, recalling that p0. Its main function is to compute the nonparametric maximum likelihood estimator of a log. A polynomial time algorithm for maximum likelihood. Samworth statistical laboratory, university of cambridge september 29, 2015 abstract the estimation of a logconcave density on rd represents a central problem in the area of nonparametric inference under shape constraints.
Jun 01, 2009 we find limiting distributions of the nonparametric maximum likelihood estimator mle of a logconcave density, i. An e cient algorithm for highdimensional logconcave maximum. This follows from the fact that the convolution of two log concave functions is log concave. Global rates of convergence in log concave density estimation arlene k. This facilitates significant speed ups up to 30000x over stateoftheart approach. Now that we can compute the maximum likelihood estimator of a multidimensional logconcave density, we can carry this method through to its natural conclusion, i. Computations related to univariate logconcave density estimation. It is an extension of the constrained newton method for nonparametric mixture estimation. Maximum likelihood estimation of a logconcave density based on censored data dominic schuhmacher institute of mathematical statistics and actuarial science university of bern joint work with lutz dumbgen and kaspar ru. Limit distribution theory for maximum likelihood estimation of a logconcave density. Pdf limit distribution theory for maximum likelihood.
Aug 24, 2018 shapeconstrained density estimation is an important topic in mathematical statistics. In this work, we study the problem of density estimation for the family of logconcave distributions on rd. Maximum likelihood estimation of a multidimensional log. Onedimensional logconcave density estimation via maximum likelihood is discussed in d. Shapeconstrained density estimation is an important topic in mathematical statistics. Density estimation of logconcave densities has been extensively investigated. Two further straightforward stability properties are as follows. This test is consistent, easy to implement, and has much improved. Adaptation in logconcave density estimation internet archive. Recent progress in logconcave density estimation richard j.
Adaptation in logconcave density estimation article pdf available in the annals of statistics 465 september 2016 with 40 reads how we measure reads. It is in the multivariate case, however, where kernel density estimation is more di. It is an extension of the constrained newton method. Maximum likelihood estimation of a multidimensional log concave.
The sum of two independent logconcave random variables is logconcave. Symmetric logconcave density estimation and mixture modeling, august 2015, joint statistical meetings, seattle, washington. Maximum likelihood estimation of the mixture of logconcave. Maximum likelihood estimation of a multi dimensional log. Under fairly general conditions, the log concave maximum likelihood estimator lcmle exists and is consistent.
We focus on densities on \\mathbb rd\ that are logconcave, and we study geometric properties of the maximum likelihood estimator mle for weighted samples. Logconcavity and density estimation lutz dumbgen, kaspar ru. Maximum likelihood estimation of a logconcave density. Despite the space of all such densities being infinitedimensional, the maximum likelihood estimate is the exponential of a piecewise linear function determined by finitely many quantities, namely the function values, or heights, at the data points. Estimation of a multidimensional logconcave density 547 fig. Recent progress in log concave density estimation richard j.
A new fast algorithm for computing the nonparametric maximum likelihood estimate of a univariate logconcave density is proposed and studied. Pdf exact solutions in logconcave maximum likelihood. We present theoretical properties of the logconcave maximum likelihood estimator of a density based on an independent and identically distributed sample in. Despite the space of all such densities being infinitedimensional, t. We present a bayesian nonparametric approach to this problem based on an exponentiated dirichlet process mixture prior and show that the posterior distribution converges to the logconcave truth at the near minimax rate in hellinger. Tentlike structure of the graph of the logarithm of the maximum likelihood estimator for bivariate data that were described in groeneboom et al. Software is readily available as r cran package fmlogcondens.
The log concave mle is the distribution with the uniform polyhedral su cient statistic. Fast multivariate logconcave density estimation sciencedirect. Maximum likelihood estimation of a logconcave density and. It is in the multivariate case, however, where kernel density estimation is more di cult and parametric models less obvious, where a logconcave model may be most useful. Maximum likelihood estimation of a logconcave density has attracted. For the continuous case, the sample complexity of the. Samworth statistical laboratory, university of cambridge september 29, 2015 abstract the estimation of a log concave density on rd represents a central problem in the area of nonparametric inference under shape constraints. Density estimation of logconcave distributions has been extensively investigated in the statistics literature dr09, gw09, wal09, dw16, bjrp, cs, ks14, bd14 with a focus on analyzing the maximum likelihood estimator mle. If a multivariate density is logconcave, so is the marginal density over any subset of variables. Background and motivation the estimation of a probability density function based on observed data is a classical and paradigmatic problem in statisticspearson1895 with a rich history see, e. Computations related to univariate logconcave density estimation lutz d umbgen university of bern kaspar ru bach university of zurich abstract maximum likelihood estimation of a logconcave density has attracted considerable attention over the last few years.
In this paper, a much more flexible mixture model is considered, which assumes each component density to be log concave. Global rates of convergence in logconcave density estimation arlene k. We present a bayesian nonparametric approach to this problem based on an exponentiated dirichlet process mixture prior and show that the posterior distribution converges to the log concave truth at the near minimax rate in hellinger distance. Logconcave density estimation is considered as an alternative for kernel density estimations which does not depend on tuning parameters. Our study covers both the case where the true underlying density is logconcave, and where this model is misspecified. Logconcave density estimation with symmetry or modal constraints by charles r. Sep 03, 2016 adaptation in logconcave density estimation article pdf available in the annals of statistics 465 september 2016 with 40 reads how we measure reads. The logconcave mle is the distribution with the uniform polyhedral su cient statistic. Logconcave density estimation with symmetry or modal. Nonparametric density estimation, shape constraint, log concave density, polya frequency function, strongly unimodal, iterative convex minorant algorithm, active set algorithm. A new fast algorithm for computing the nonparametric maximum likelihood estimate of a univariate log. An e cient algorithm for highdimensional logconcave. Likelihood estimation of a multivariate logconcave density madeleine cule, robert gramacy and richard samworth university of cambridge abstract in this document we introduce the r package logconcdead logconcave density estimation in arbitrary dimensions. A similar rate is seen for the one dimensional nonparametric maximum likelihood estimator npmle of a logconcave density, which was recently proved to be n25, as long as the density is strictly log.
In recent years, log concave density estimation via maximum likelihood estimation has emerged as a fascinating alternative to traditional nonparametric smoothing techniques, such as kernel density estimation, which require the choice of one or more bandwidths. Maximum likelihood estimation of the mixture of log. Expandingthescopeofourinvestigation, we now replace e. Several algorithms have been proposed to estimate such a density. If a multivariate density is log concave, so is the marginal density over any subset of variables. This follows from the fact that the convolution of two logconcave functions is logconcave. Global rates of convergence in logconcave density estimation. If f0 2p0 satis es f0x0 0, f0 00x0 estimation of a multivariate log concave density madeleine cule statistical laboratory, dpmms and emmanuel college university of cambridge this dissertation is submitted for the degree of doctor of philosophy 14 september 2009. In recent years, logconcave density estimation via maximum likelihood estimation has emerged as a fascinating alternative to traditional nonparametric smoothing techniques, such as kernel density estimation, which require the choice of one or more bandwidths. A main drawback is the strong parametric assumption about the component densities. Now that we can compute the maximum likelihood estimator of a multi. Recent progress in logconcave density estimation arxiv.
Limit distribution theory for maximum likelihood estimation. Cule, samworth, and stewart showed that the logarithm of the optimal log concave density is piecewise linear and supported on a regular. A likelihood ratio test for the location of the maximum of a concave function, july 2015, european meeting of statisticians, amsterdam, netherlands. We explore in what sense exact solutions to this problem are possible. In recent years, logconcave density estimation via maximum likelihood estimation has emerged as a fascinating. A faster algorithm for computing the log concave mle. Geometry of logconcave density estimation springerlink. We find limiting distributions of the nonparametric maximum likelihood estimator mle of a logconcave density, i. A polynomial time algorithm for maximum likelihood estimation. Smoothed logconcave maximum likelihood estimation 75 density. Fast nearoptimal solver for multivariate logconcave density estimation. Nonparametric inference about a densitys mode and shape constraints.