bckdengpdcon: Boundary Corrected Kernel Density Estimate and GPD Tail...

In evmix: Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

Description

Density, cumulative distribution function, quantile function and random number generation for the extreme value mixture model with boundary corrected kernel density estimate for bulk distribution upto the threshold and conditional GPD above threshold with continuity at threshold. The parameters are the bandwidth lambda, threshold u GPD shape xi and tail fraction phiu.

Usage

dbckdengpdcon(x, kerncentres, lambda = NULL,
  u = as.vector(quantile(kerncentres, 0.9)), xi = 0, phiu = TRUE,
  bw = NULL, kernel = "gaussian", bcmethod = "simple",
  proper = TRUE, nn = "jf96", offset = NULL, xmax = NULL,
  log = FALSE)

pbckdengpdcon(q, kerncentres, lambda = NULL,
  u = as.vector(quantile(kerncentres, 0.9)), xi = 0, phiu = TRUE,
  bw = NULL, kernel = "gaussian", bcmethod = "simple",
  proper = TRUE, nn = "jf96", offset = NULL, xmax = NULL,
  lower.tail = TRUE)

qbckdengpdcon(p, kerncentres, lambda = NULL,
  u = as.vector(quantile(kerncentres, 0.9)), xi = 0, phiu = TRUE,
  bw = NULL, kernel = "gaussian", bcmethod = "simple",
  proper = TRUE, nn = "jf96", offset = NULL, xmax = NULL,
  lower.tail = TRUE)

rbckdengpdcon(n = 1, kerncentres, lambda = NULL,
  u = as.vector(quantile(kerncentres, 0.9)), xi = 0, phiu = TRUE,
  bw = NULL, kernel = "gaussian", bcmethod = "simple",
  proper = TRUE, nn = "jf96", offset = NULL, xmax = NULL)

Arguments

x	quantiles
kerncentres	kernel centres (typically sample data vector or scalar)
lambda	bandwidth for kernel (as half-width of kernel) or NULL
u	threshold
xi	shape parameter
phiu	probability of being above threshold [0, 1] or TRUE
bw	bandwidth for kernel (as standard deviations of kernel) or NULL
kernel	kernel name (default = "gaussian")
bcmethod	boundary correction method
proper	logical, whether density is renormalised to integrate to unity (where needed)
nn	non-negativity correction method (simple boundary correction only)
offset	offset added to kernel centres (logtrans only) or NULL
xmax	upper bound on support (copula and beta kernels only) or NULL
log	logical, if TRUE then log density
q	quantiles
lower.tail	logical, if FALSE then upper tail probabilities
p	cumulative probabilities
n	sample size (positive integer)

Details

Extreme value mixture model combining boundary corrected kernel density (BCKDE) estimate for the bulk below the threshold and GPD for upper tail with continuity at threshold. The user chooses from a wide range of boundary correction methods designed to cope with a lower bound at zero and potentially also both upper and lower bounds.

Some boundary correction methods require a secondary correction for negative density estimates of which two methods are implemented. Further, some methods don't necessarily give a density which integrates to one, so an option is provided to renormalise to be proper.

It assumes there is a lower bound at zero, so prior transformation of data is required for a alternative lower bound (possibly including negation to allow for an upper bound).

The user can pre-specify phiu permitting a parameterised value for the tail fraction φ_u. Alternatively, when phiu=TRUE the tail fraction is estimated as the tail fraction from the BCKDE bulk model.

The alternate bandwidth definitions are discussed in the kernels, with the lambda as the default. The bw specification is the same as used in the density function.

The possible kernels are also defined in kernels with the "gaussian" as the default choice.

The cumulative distribution function with tail fraction φ_u defined by the upper tail fraction of the BCKDE (phiu=TRUE), upto the threshold x ≤ u, given by:

F(x) = H(x)

and above the threshold x > u:

F(x) = H(u) + [1 - H(u)] G(x)

where H(x) and G(X) are the BCKDE and conditional GPD cumulative distribution functions respectively.

The cumulative distribution function for pre-specified φ_u, upto the threshold x ≤ u, is given by:

F(x) = (1 - φ_u) H(x)/H(u)

and above the threshold x > u:

F(x) = φ_u + [1 - φ_u] G(x)

Notice that these definitions are equivalent when φ_u = 1 - H(u).

The continuity constraint means that (1 - φ_u) h(u)/H(u) = φ_u g(u) where h(x) and g(x) are the BCKDE and conditional GPD density functions respectively. The resulting GPD scale parameter is then:

σ_u = φ_u H(u) / [1 - φ_u] h(u)

. In the special case of where the tail fraction is defined by the bulk model this reduces to

σ_u = [1 - H(u)] / h(u)

.

Unlike the standard KDE, there is no general rule-of-thumb bandwidth for all the BCKDE, with only certain methods having a guideline in the literature, so none have been implemented. Hence, a bandwidth must always be specified and you should consider using fbckdengpdcon of fbckden function for cross-validation MLE for bandwidth.

See gpd for details of GPD upper tail component and dbckden for details of BCKDE bulk component.

Value

dbckdengpdcon gives the density, pbckdengpdcon gives the cumulative distribution function, qbckdengpdcon gives the quantile function and rbckdengpdcon gives a random sample.

Boundary Correction Methods

See dbckden for details of BCKDE methods.

Warning

The "simple", "renorm", "beta1", "beta2", "gamma1" and "gamma2" boundary correction methods may require renormalisation using numerical integration which can be very slow. In particular, the numerical integration is extremely slow for the kernel="uniform", due to the adaptive quadrature in the integrate function being particularly slow for functions with step-like behaviour.

Acknowledgments

Based on code by Anna MacDonald produced for MATLAB.

Note

Unlike most of the other extreme value mixture model functions the bckdengpdcon functions have not been vectorised as this is not appropriate. The main inputs (x, p or q) must be either a scalar or a vector, which also define the output length. The kerncentres can also be a scalar or vector.

The kernel centres kerncentres can either be a single datapoint or a vector of data. The kernel centres (kerncentres) and locations to evaluate density (x) and cumulative distribution function (q) would usually be different.

Default values are provided for all inputs, except for the fundamentals kerncentres, x, q and p. The default sample size for rbckdengpdcon is 1.

The xmax option is only relevant for the beta and copula methods, so a warning is produced if this is not NULL for in other methods. The offset option is only relevant for the "logtrans" method, so a warning is produced if this is not NULL for in other methods.

Missing (NA) and Not-a-Number (NaN) values in x, p and q are passed through as is and infinite values are set to NA. None of these are not permitted for the parameters or kernel centres.

Error checking of the inputs (e.g. invalid probabilities) is carried out and will either stop or give warning message as appropriate.

Author(s)

Yang Hu and Carl Scarrott [e-mail].

References

http://en.wikipedia.org/wiki/Kernel_density_estimation

http://en.wikipedia.org/wiki/Generalized_Pareto_distribution

Scarrott, C.J. and MacDonald, A. (2012). A review of extreme value threshold estimation and uncertainty quantification. REVSTAT - Statistical Journal 10(1), 33-59. Available from http://www.ine.pt/revstat/pdf/rs120102.pdf

Bowman, A.W. (1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika 71(2), 353-360.

Duin, R.P.W. (1976). On the choice of smoothing parameters for Parzen estimators of probability density functions. IEEE Transactions on Computers C25(11), 1175-1179.

MacDonald, A., Scarrott, C.J., Lee, D., Darlow, B., Reale, M. and Russell, G. (2011). A flexible extreme value mixture model. Computational Statistics and Data Analysis 55(6), 2137-2157.

MacDonald, A., C. J. Scarrott, and D. S. Lee (2011). Boundary correction, consistency and robustness of kernel densities using extreme value theory. Submitted. Available from: http://www.math.canterbury.ac.nz/~c.scarrott.

Wand, M. and Jones, M.C. (1995). Kernel Smoothing. Chapman && Hall.

See Also

gpd, kernels, kfun, density, bw.nrd0 and dkde in ks package.

Other kdengpdcon: fbckdengpdcon, fgkgcon, fkdengpdcon, fkdengpd, gkgcon, kdengpdcon, kdengpd

Other bckden: bckdengpd, bckden, fbckdengpdcon, fbckdengpd, fbckden, fkden, kden

Other bckdengpd: bckdengpd, bckden, fbckdengpdcon, fbckdengpd, fbckden, fkdengpd, gkg, kdengpd, kden

Other bckdengpdcon: bckdengpd, bckden, fbckdengpdcon, fbckdengpd, fbckden, fkdengpdcon, gkgcon, kdengpdcon

Other fbckdengpdcon: fbckdengpdcon

Examples

## Not run: 
set.seed(1)
par(mfrow = c(2, 2))

kerncentres=rgamma(500, shape = 1, scale = 2)
xx = seq(-0.1, 10, 0.01)
hist(kerncentres, breaks = 100, freq = FALSE)
lines(xx, dbckdengpdcon(xx, kerncentres, lambda = 0.5, bcmethod = "reflect"),
xlab = "x", ylab = "f(x)")
abline(v = quantile(kerncentres, 0.9))

plot(xx, pbckdengpdcon(xx, kerncentres, lambda = 0.5, bcmethod = "reflect"),
xlab = "x", ylab = "F(x)", type = "l")
lines(xx, pbckdengpdcon(xx, kerncentres, lambda = 0.5, xi = 0.3, bcmethod = "reflect"),
xlab = "x", ylab = "F(x)", col = "red")
lines(xx, pbckdengpdcon(xx, kerncentres, lambda = 0.5, xi = -0.3, bcmethod = "reflect"),
xlab = "x", ylab = "F(x)", col = "blue")
legend("topleft", paste("xi =",c(0, 0.3, -0.3)),
      col=c("black", "red", "blue"), lty = 1, cex = 0.5)

kerncentres = rweibull(1000, 2, 1)
x = rbckdengpdcon(1000, kerncentres, lambda = 0.1, phiu = TRUE, bcmethod = "reflect")
xx = seq(0.01, 3.5, 0.01)
hist(x, breaks = 100, freq = FALSE)         
lines(xx, dbckdengpdcon(xx, kerncentres, lambda = 0.1, phiu = TRUE, bcmethod = "reflect"),
xlab = "x", ylab = "f(x)")

lines(xx, dbckdengpdcon(xx, kerncentres, lambda = 0.1, xi=-0.2, phiu = 0.1, bcmethod = "reflect"),
xlab = "x", ylab = "f(x)", col = "red")
lines(xx, dbckdengpdcon(xx, kerncentres, lambda = 0.1, xi=0.2, phiu = 0.1, bcmethod = "reflect"),
xlab = "x", ylab = "f(x)", col = "blue")
legend("topleft", c("xi = 0", "xi = 0.2", "xi = -0.2"),
      col=c("black", "red", "blue"), lty = 1)

## End(Not run)

evmix documentation built on Sept. 3, 2019, 5:07 p.m.