mlelcd: Compute the maximum likelihood estimator of a log-concave...
In LogConcDEAD: Log-Concave Density Estimation in Arbitrary Dimensions

mlelcd

R Documentation

Compute the maximum likelihood estimator of a log-concave density

Description

Uses Shor's r-algorithm to compute the maximum likelihood estimator of a log-concave density based on an i.i.d. sample. The estimator is uniquely determined by its value at the data points. The output is an object of class "LogConcDEAD" which contains all the information needed to plot the estimator using the plot method, or to evaluate it using the function dlcd.

Usage

mlelcd(x, w=rep(1/nrow(x),nrow(x)), y=initialy(x),
  verbose=-1, alpha=5, c=1, sigmatol=10^-8, integraltol=10^-4,
  ytol=10^-4, Jtol=0.001, chtol=10^-6)

Arguments

x

Data in R^d, in the form of an n \times d numeric matrix

w

Vector of weights w_i such that the computed estimator maximizes

\sum_{i=1}^n w_i \log f(x_i)

subject to the restriction that f is log-concave. The default is \frac{1}{n} for all i, which corresponds to i.i.d. observations.

`y`	Vector giving starting point for the `r`-algorithm. If none given, a kernel estimate is used.
`verbose`	-1: (default) prints nothing 0: prints warning messages `n>0`: prints summary information every `n` iterations
`alpha`	Scalar parameter for SolvOpt
`c`	Scalar giving starting step size
`sigmatol`	Real-valued scalar giving one of the stopping criteria: Relative change in `\sigma` must be below `sigmatol` for algorithm to terminate. (See Details)
`ytol`	Real-valued scalar giving on of the stopping criteria: Relative change in `y` must be below `ytol` for algorithm to terminate. (See Details)
`integraltol`	Real-valued scalar giving one of the stopping criteria: `\| 1 - \exp(\bar{h}_y) \|` must be below `integraltol` for algorithm to terminate. (See Details)
`Jtol`	Parameter controlling when Taylor expansion is used in computing the function `\sigma`
`chtol`	Parameter controlling convex hull computations

Details

The log-concave maximum likelihood density estimator based on data X_1, \ldots, X_n is the function that maximizes

\sum_{i=1}^n w_i \log f(X_i)

subject to the constraint that f is log-concave. For i.i.d.~data, the weights w_i should be \frac{1}{n} for each i.

This is a function of the form \bar{h}_y for some y \in R^n, where

\bar{h}_y(x) = \inf \lbrace h(x) \colon h \textrm{ concave }, h(x_i) \geq y_i \textrm{ for } i = 1, \ldots, n \rbrace.

Functions of this form may equivalently be specified by dividing C_n, the convex hull of the data, into simplices C_j for j \in J (triangles in 2d, tetrahedra in 3d etc), and setting

f(x) = \exp\{b_j^T x - \beta_j\}

for x \in C_j, and f(x) = 0 for x \notin C_n.

This function uses Shor's r-algorithm (an iterative subgradient-based procedure) to minimize over vectors y in R^n the function

\sigma(y) = -\frac{1}{n} \sum_{i=1}^n y_i + \int \exp(\bar{h}_y(x)) \, dx.

This is equivalent to finding the log-concave maximum likelihood estimator, as demonstrated in Cule, Samworth and Stewart (2008).

An implementation of Shor's r-algorithm based on SolvOpt is used.

Computing \sigma makes use of the qhull library. Code from this C-based library is copied here as it is not currently possible to use compiled code from another library. For points not in general position, this requires a Taylor expansion of \sigma, discussed in Cule and D\"umbgen (2008).

Value

An object of class "LogConcDEAD", with the following components:

`x`	Data copied from input (may be reordered)
`w`	weights copied from input (may be reordered)
`logMLE`	`vector` of the log of the maximum likelihood estimate, evaluated at the observation points
`NumberOfEvaluations`	Vector containing the number of steps, number of function evaluations, and number of subgradient evaluations. If the SolvOpt algorithm fails, the first component will be an error code `(<0)`.
`MinSigma`	Real-valued scalar giving minimum value of the objective function
`b`	`matrix` (see Details)
`beta`	`vector` (see Details)
`triang`	`matrix` containing final triangulation of the convex hull of the data
`verts`	`matrix` containing details of triangulation for use in `dlcd`
`vertsoffset`	`matrix` containing details of triangulation for use in `dlcd`
`chull`	Vector containing vertices of faces of the convex hull of the data
`outnorm`	`matrix` where each row is an outward pointing normal vectors for the faces of the convex hull of the data. The number of vectors depends on the number of faces of the convex hull.
`outoffset`	`matrix` where each row is a point on a face of the convex hull of the data. The number of vectors depends on the number of faces of the convex hull.

Note

For one-dimensional data, the active set algorithm of logcondens is faster, and may be preferred.

The authors gratefully acknowledge the assistance of Lutz Duembgen at the University of Bern for his insight into the objective function \sigma.

Further references, including definitions and background material, may be found in Cule, Samworth and Stewart (2010).

Author(s)

Madeleine Cule

Robert B. Gramacy

Richard Samworth

Yining Chen

References

Barber, C.B., Dobkin, D.P., and Huhdanpaa, H.T. (1996) The Quickhull algorithm for convex hulls ACM Trans. on Mathematical Software, 22(4) p.469-483 http://www.qhull.org

Cule, M. L. and D\"umbgen, L. (2008) On an auxiliary function for log-density estimation, University of Bern technical report. https://arxiv.org/abs/0807.4719

Cule, M. L., Samworth, R. J., and Stewart, M. I. (2010) Maximum likelihood estimation of a log-concave density, Journal of the Royal Statistical Society, Series B, 72(5) p.545-607.

Kappel, F. and Kuntsevich, A. V. (2000) An implementation of Shor's r-algorithm, Computational Optimization and Applications, Volume 15, Issue 2, 193-205.

Shor, N. Z. (1985) Minimization methods for nondifferentiable functions, Springer-Verlag

Examples

## Some simple normal data, and a few plots

x <- matrix(rnorm(200),ncol=2)
lcd <- mlelcd(x)
g <- interplcd(lcd)

oldpar <- par(mfrow = c(1,1))
par(mfrow=c(2,2), ask=TRUE)
plot(lcd, g=g, type="c")
plot(lcd, g=g, type="c", uselog=TRUE)
plot(lcd, g=g, type="i")
plot(lcd, g=g, type="i", uselog=TRUE)
par(oldpar)

## 2D interactive plot (need rgl package, not run here)
if(interactive()) {plot(lcd, type="r")}


## Some plots of marginal estimates
g.marg1 <- interpmarglcd(lcd, marg=1)
g.marg2 <- interpmarglcd(lcd, marg=2)
plot(lcd, marg=1, g.marg=g.marg1)
plot(lcd, marg=2, g.marg=g.marg2) 

## generate some points from the fitted density
## via independent rejection sampling
generated1 <- rlcd(100, lcd)
colMeans(generated1)
## via Metropolis-Hastings algorithm
generated2 <- rlcd(100, lcd, "MH")
colMeans(generated2)

## evaluate the fitted density
mypoint <- c(0, 0)
dlcd(mypoint, lcd, uselog=FALSE)
mypoint <- c(1, 0)
dlcd(mypoint, lcd, uselog=FALSE)

## evaluate the marginal density
dmarglcd(0, lcd, marg=1)
dmarglcd(1, lcd, marg=2)

## evaluate the covariance matrix of the fitted density
covariance <- cov.LogConcDEAD(lcd)

## find the hat matrix for the smoothed log-concave that
## matches empirical mean and covariance
A <- hatA(lcd)

## evaluate the fitted smoothed log-concave density
mypoint <- c(0, 0)
dslcd(mypoint, lcd, A)
mypoint <- c(1, 0)
dslcd(mypoint, lcd, A)

## generate some points from the fitted smoothed log-concave density
generated <- rslcd(100, lcd, A)

LogConcDEAD documentation built on April 6, 2023, 1:11 a.m.