npuniden.sc: Kernel Shape Constrained Bounded Univariate Density...

In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Kernel Shape Constrained Bounded Univariate Density Estimation

Description

npuniden.sc computes shape constrained kernel univariate unconditional density estimates given a vector of continuously distributed training data and a bandwidth. Lower and upper bounds [a,b] can be supplied (default is [0,1]) and if a is set to -Inf there is only one bound on the right, while if b is set to Inf there is only one bound on the left.

Usage

npuniden.sc(X = NULL,
            Y = NULL,
            h = NULL,
            a = 0,
            b = 1,
            lb = NULL,
            ub = NULL,
            extend.range = 0,
            num.grid = 0,
            function.distance = TRUE,
            integral.equal = FALSE,
            constraint = c("density",
                           "mono.incr",
                           "mono.decr",
                           "concave",
                           "convex",
                           "log-concave",
                           "log-convex"))

Arguments

X	a required numeric vector of training data lying in [a,b]
Y	an optional numeric vector of evaluation data lying in [a,b]
h	a bandwidth (>0)
a	an optional lower bound on the support of X or Y (defaults to 0)
b	an optional upper bound on the support of X or Y (defaults to 1)
lb	a scalar lower bound (≥ 0) to be used in conjunction with constraint="density"
ub	a scalar upper bound (≥ 0 and ≥ lb) to be used in conjunction with constraint="density"
extend.range	number specifying the fraction by which the range of the training data should be extended for the additional grid points (passed to the function extendrange)
num.grid	number of additional grid points (in addition to X and Y) placed on an equi-spaced grid spanning extendrange(c(X,Y),f=extend.range) (if num.grid=0 no additional grid points will be used regardless of the value of extend.range)
function.distance	a logical value that, if TRUE, minimizes the squared deviation between the constrained and unconstrained estimates, otherwise, minimizes the squared deviation between the constrained and unconstrained weights
integral.equal	a logical value, that, if TRUE, adjusts the constrained estimate to have the same probability mass over the range X, Y, and the additional grid points
constraint	a character string indicating whether the estimate is to be constrained to be monotonically increasing (constraint="mono.incr"), decreasing (constraint="mono.incr"), convex (constraint="convex"), concave (constraint="concave"), log-convex (constraint="log-convex"), or log-concave (constraint="log-concave")

Details

Typical usages are (see below for a complete list of options and also the examples at the end of this help file)

    model <- npuniden.sc(X,a=-2,b=3)
  

npuniden.sc implements a methods for estimating a univariate density function defined over a continuous random variable in the presence of bounds subject to a variety of shape constraints. The bounded estimates use the truncated Gaussian kernel function.

Note that for the log-constrained estimates, the derivative estimate returned is that for the log-constrained estimate not the non-log value of the estimate returned by the function. See Example 5 below hat manually plots the log-density and returned derivative (no transformation is needed when plotting the density estimate itself).

If the quadratic program solver fails to find a solution, the unconstrained estimate is returned with an immediate warning. Possible causes to be investigated are undersmoothing, sparsity, and the presence of non-sample grid points. To investigate the possibility of undersmoothing try using a larger bandwidth, to investigate sparsity try decreasing extend.range, and to investigate non-sample grid points try setting num.grid to 0.

Mean square error performance seems to improve generally when using additional grid points in the empirical support of X and Y (i.e., in the observed range of the data sample) but appears to deteriorate when imposing constraints beyond the empirical support (i.e., when extend.range is positive). Increasing the number of additional points beyond a hundred or so appears to have a limited impact.

The option function.distance=TRUE appears to perform better for imposing convexity, concavity, log-convexity and log-concavity, while function.distance=FALSE appears to perform better for imposing monotonicity, whether increasing or decreasing (based on simulations for the Beta(s1,s2) distribution with sample size n=100).

Value

A list with the following elements:

f	unconstrained density estimate
f.sc	shape constrained density estimate
se.f	asymptotic standard error of the unconstrained density estimate
se.f.sc	asymptotic standard error of the shape constrained density estimate
f.deriv	unconstrained derivative estimate (of order 1 or 2 or log thereof)
f.sc.deriv	shape constrained derivative estimate (of order 1 or 2 or log thereof)
F	unconstrained distribution estimate
F.sc	shape constrained distribution estimate
integral.f	the integral of the unconstrained estimate over X, Y, and the additional grid points
integral.f.sc	the integral of the constrained estimate over X, Y, and the additional grid points
solve.QP	logical, if TRUE solve.QP succeeded, otherwise failed
attempts	number of attempts when solve.QP fails (max = 9)

Author(s)

Jeffrey S. Racine [e-mail]

References

Du, P. and C. Parmeter and J. Racine, “Shape Constrained Kernel Density Estimation”, manuscript.

See Also

The logcondens, LogConDEAD, and scdensity packages, and the function npuniden.boundary.

Examples

## Not run: 
n <- 100
set.seed(42)

## Example 1: N(0,1), constrain the density to lie within lb=.1 and ub=.2

X <- sort(rnorm(n))
h <- npuniden.boundary(X,a=-Inf,b=Inf)$h
foo <- npuniden.sc(X,h=h,constraint="density",a=-Inf,b=Inf,lb=.1,ub=.2)
ylim <- range(c(foo$f.sc,foo$f))
plot(X,foo$f.sc,type="l",ylim=ylim,xlab="X",ylab="Density")
lines(X,foo$f,col=2,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

## Example 2: Beta(5,1), DGP is monotone increasing, impose valid
## restriction

X <- sort(rbeta(n,5,1))
h <- npuniden.boundary(X)$h

foo <- npuniden.sc(X=X,h=h,constraint=c("mono.incr"))

par(mfrow=c(1,2))
ylim <- range(c(foo$f.sc,foo$f))
plot(X,foo$f.sc,type="l",ylim=ylim,xlab="X",ylab="Density")
lines(X,foo$f,col=2,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

ylim <- range(c(foo$f.sc.deriv,foo$f.deriv))
plot(X,foo$f.sc.deriv,type="l",ylim=ylim,xlab="X",ylab="First Derivative")
lines(X,foo$f.deriv,col=2,lty=2)
abline(h=0,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

## Example 3: Beta(1,5), DGP is monotone decreasing, impose valid
## restriction

X <- sort(rbeta(n,1,5))
h <- npuniden.boundary(X)$h

foo <- npuniden.sc(X=X,h=h,constraint=c("mono.decr"))

par(mfrow=c(1,2))
ylim <- range(c(foo$f.sc,foo$f))
plot(X,foo$f.sc,type="l",ylim=ylim,xlab="X",ylab="Density")
lines(X,foo$f,col=2,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

ylim <- range(c(foo$f.sc.deriv,foo$f.deriv))
plot(X,foo$f.sc.deriv,type="l",ylim=ylim,xlab="X",ylab="First Derivative")
lines(X,foo$f.deriv,col=2,lty=2)
abline(h=0,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")
 
## Example 4: N(0,1), DGP is log-concave, impose invalid concavity
## restriction

X <- sort(rnorm(n))
h <- npuniden.boundary(X,a=-Inf,b=Inf)$h

foo <- npuniden.sc(X=X,h=h,a=-Inf,b=Inf,constraint=c("concave"))

par(mfrow=c(1,2))
ylim <- range(c(foo$f.sc,foo$f))
plot(X,foo$f.sc,type="l",ylim=ylim,xlab="X",ylab="Density")
lines(X,foo$f,col=2,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")
ylim <- range(c(foo$f.sc.deriv,foo$f.deriv))

plot(X,foo$f.sc.deriv,type="l",ylim=ylim,xlab="X",ylab="Second Derivative")
lines(X,foo$f.deriv,col=2,lty=2)
abline(h=0,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

## Example 45: Beta(3/4,3/4), DGP is convex, impose valid restriction

X <- sort(rbeta(n,3/4,3/4))
h <- npuniden.boundary(X)$h

foo <- npuniden.sc(X=X,h=h,constraint=c("convex"))

par(mfrow=c(1,2))
ylim <- range(c(foo$f.sc,foo$f))
plot(X,foo$f.sc,type="l",ylim=ylim,xlab="X",ylab="Density")
lines(X,foo$f,col=2,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

ylim <- range(c(foo$f.sc.deriv,foo$f.deriv))
plot(X,foo$f.sc.deriv,type="l",ylim=ylim,xlab="X",ylab="Second Derivative")
lines(X,foo$f.deriv,col=2,lty=2)
abline(h=0,lty=2)
rug(X)
legend("topleft",c("Constrained","Unconstrained"),lty=1:2,col=1:2,bty="n")

## Example 6: N(0,1), DGP is log-concave, impose log-concavity
## restriction

X <- sort(rnorm(n))
h <- npuniden.boundary(X,a=-Inf,b=Inf)$h

foo <- npuniden.sc(X=X,h=h,a=-Inf,b=Inf,constraint=c("log-concave"))

par(mfrow=c(1,2))

ylim <- range(c(log(foo$f.sc),log(foo$f)))
plot(X,log(foo$f.sc),type="l",ylim=ylim,xlab="X",ylab="Log-Density")
lines(X,log(foo$f),col=2,lty=2)
rug(X)
legend("topleft",c("Constrained-log","Unconstrained-log"),lty=1:2,col=1:2,bty="n")

ylim <- range(c(foo$f.sc.deriv,foo$f.deriv))
plot(X,foo$f.sc.deriv,type="l",ylim=ylim,xlab="X",ylab="Second Derivative of Log-Density")
lines(X,foo$f.deriv,col=2,lty=2)
abline(h=0,lty=2)
rug(X)
legend("topleft",c("Constrained-log","Unconstrained-log"),lty=1:2,col=1:2,bty="n")

## End(Not run) 

np documentation built on Oct. 19, 2022, 1:08 a.m.