game: Smooth Parameter Estimation and Bootstrapping of Generalized...

In ismev: An Introduction to Statistical Modeling of Extreme Values

Description

gamGPDfit() fits the parameters of a generalized Pareto distribution (GPD) depending on covariates in a non- or semiparametric way.

gamGPDboot() fits and bootstraps the parameters of a GPD distribution depending on covariates in a non- or semiparametric way. Applies the post-blackend bootstrap of Chavez-Demoulin and Davison (2005).

Usage

gamGPDfit(x, threshold, nexc=NULL, datvar, xiFrhs, nuFrhs,
          init=gpd.fit(x[, datvar], threshold=threshold, show=FALSE)$mle[2:1],
          niter=32, include.updates=FALSE, epsxi=1e-05, epsnu=1e-05,
          progress=TRUE, verbose=FALSE, ...)
gamGPDboot(x, B, threshold, nexc=NULL, datvar, xiFrhs, nuFrhs,
           init=gpd.fit(x[, datvar], threshold=threshold, show=FALSE)$mle[2:1],
           niter=32, include.updates=FALSE, epsxi=1e-5, epsnu=1e-5,
           boot.progress=TRUE, progress=FALSE, verbose=FALSE, debug=FALSE, ...)

Arguments

x	data.frame containing the losses (in some component; can be specified with the argument datvar; the other components contain the covariates).
B	number of bootstrap replications.
threshold	threshold of the peaks-over-threshold (POT) method.
nexc	number of excesses. This can be used to determine
datvar	name of the data column in x which contains the the data to be modeled.
xiFrhs	right-hand side of the formula for xi in the gam() call for fitting xi.
nuFrhs	right-hand side of the formula for nu in the gam() call for fitting nu.
init	bivariate vector containing initial values for (xi, beta).
niter	maximal number of iterations in the backfitting algorithm.
include.updates	logical indicating whether updates for xi and nu are returned as well (note: this might lead to objects of large size).
epsxi	epsilon for stop criterion for xi.
epsnu	epsilon for stop criterion for nu.
boot.progress	logical indicating whether progress information about gamGPDboot() is displayed.
progress	logical indicating whether progress information about gamGPDfit() is displayed. For gamGPDboot(), progress is only passed to gamGPDfit() in the case that boot.progress==TRUE.
verbose	logical indicating whether additional information (in case of undesired behavior) is printed. For gamGPDboot(), progress is only passed to gamGPDfit() if boot.progress==TRUE.
debug	logical indicating whether initial fit (before the bootstrap is initiated) is saved.
...	additional arguments passed to gam() (which is called internally; see the source code of gamGPDfitUp()).

Details

gamGPDfit() fits the parameters xi and beta of the generalized Pareto distribution GPD(xi,beta) depending on covariates in a non- or semiparametric way. The distribution function is given by

G[xi,beta](x)=1-(1+xi x/beta)^(-1/xi), x>=0,

for xi>0 (which is what we assume) and beta>0. Note that β is also denoted by σ in this package. Estimation of xi and beta by gamGPDfit() is done via penalized maximum likelihood estimation, where the estimators are computed with a backfitting algorithm. In order to guarantee convergence of this algorithm, a reparameterization of beta in terms of the parameter nu is done via

beta=exp(nu)/(1+xi).

The parameters xi and nu (and thus beta) are allowed to depend on covariates (including time) in a non- or semiparametric way, for example:

xi=xi(x,t)=x^Talpha[xi]+h[xi](t),

nu=nu(x,t)=x^Talpha[nu]+h[nu](t),

where x denotes the vector of covariates, alpha[xi], alpha[nu] are parameter vectors and h[xi], h[nu] are regression splines. For more details, see the references and the source code.

gamGPDboot() first fits the GPD parameters via gamGPDfit(). It then conducts the post-blackend bootstrap of Chavez-Demoulin and Davison (2005). To this end, it computes the residuals, resamples them (B times), reconstructs the corresponding excesses, and refits the GPD parameters via gamGPDfit() again.

Value

gamGPDfit() returns a list with the components

xi:

estimated parameters xi;

beta:

estimated parameters beta;

nu:

estimated parameters nu;

se.xi:

standard error for xi ((possibly adjusted) second-order derivative of the reparameterized log-likelihood with respect to xi) multiplied by -1;

se.nu:

standard error for nu ((possibly adjusted) second-order derivative of the reparameterized log-likelihood with respect to nu) multiplied by -1;

xi.covar:

(unique) covariates for xi;

nu.covar:

(unique) covariates for nu;

covar:

available covariate combinations used for fitting beta(xi, nu);

y:

vector of excesses (exceedances minus threshold);

res:

residuals;

MRD:

mean relative distances between for all iterations, calculated between old parameters (xi, nu) (from the last iteration) and new parameters (currently estimated ones);

logL:

log-likelihood at the estimated parameters;

xiObj:

R object of type gamObject for estimated xi (returned by mgcv::gam());

nuObj:

R object of type gamObject for estimated nu (returned by mgcv::gam());

xiUpdates:

if include.updates is TRUE, updates for xi for each iteration. This is a list of R objects of type gamObject which contains xiObj as last element;

nuUpdates:

if include.updates is TRUE, updates for nu for each iteration. This is a list of R objects of type gamObject which contains nuObj as last element;

gamGPDboot() returns a list of length B+1 where the first component contains the results of the initial fit via gamGPDfit() and the other B components contain the results for each replication of the post-blackend bootstrap.

Author(s)

Marius Hofert, Valerie Chavez-Demoulin.

References

Chavez-Demoulin, V., and Davison, A. C. (2005), Generalized additive models for sample extremes, Applied Statistics 54(1), 207–222.

Chavez-Demoulin, V., and Hofert, M. (to be submitted), Smooth extremal models fitted by penalized maximum likelihood estimation.

Examples

### Example 1: fitting capability ##############################################

## generate an example data set
years <- 2003:2012 # years
nyears <- length(years)
n <- 250 # sample size for each (different) xi
u <- 200 # threshold
rGPD <- function(n, xi, beta) ((1-runif(n))^(-xi)-1)*beta/xi # sampling GPD

set.seed(17) # setting seed
xi.true.A <- seq(0.4, 0.8, length=nyears) # true xi for group "A"
## generate losses for group "A"
lossA <- unlist(lapply(1:nyears,
                       function(y) u + rGPD(n, xi=xi.true.A[y], beta=1)))
xi.true.B <- xi.true.A^2 # true xi for group "B"
## generate losses for group "B"
lossB <- unlist(lapply(1:nyears,
                       function(y) u + rGPD(n, xi=xi.true.B[y], beta=1)))
## build data frame
time <- rep(rep(years, each=n), 2) # "2" stands for the two groups
covar <- rep(c("A","B"), each=n*nyears)
value <- c(lossA, lossB)
x <- data.frame(covar=covar, time=time, value=value)

## fit
eps <- 1e-3 # to decrease the run time for this example
fit <- gamGPDfit(x, threshold=u, datvar="value", xiFrhs=~covar+s(time)-1,
                 nuFrhs=~covar+s(time)-1, epsxi=eps, epsnu=eps)
## note: choosing s(..., bs="cr") will fit cubic splines

## grab the fitted values per group and year
xi.fit <- fitted(fit$xiObj)
xi.fit. <- xi.fit[1+(0:(2*nyears-1))*n] # pick fit for each group and year
xi.fit.A <- xi.fit.[1:nyears] # fit for "A" and each year
xi.fit.B <- xi.fit.[(nyears+1):(2*nyears)] # fit for "B" and each year

## plot the fitted values of xi and the true ones we simulated from
par(mfrow=c(1,2))
plot(years, xi.true.A, type="l", ylim=range(xi.true.A, xi.fit.A),
     main="Group A", xlab="Year", ylab=expression(xi))
points(years, xi.fit.A, type="l", col="red")
legend("topleft", inset=0.04, lty=1, col=c("black", "red"),
       legend=c("true", "fitted"), bty="n")
plot(years, xi.true.B, type="l", ylim=range(xi.true.B, xi.fit.B),
     main="Group B", xlab="Year", ylab=expression(xi))
points(years, xi.fit.B, type="l", col="blue")
legend("topleft", inset=0.04, lty=1, col=c("black", "blue"),
       legend=c("true", "fitted"), bty="n")

## Not run: 
### Example 2: Comparison of (the more general) gamGPDfit() with gpd.fit() ########

set.seed(17) # setting seed
xi.true.A <- rep(0.4, length=nyears)
xi.true.B <- rep(0.8, length=nyears)
## generate losses for group "A"
lossA <- unlist(lapply(1:nyears,
                       function(y) u + rGPD(n, xi=xi.true.A[y], beta=1)))
## generate losses for group "B"
lossB <- unlist(lapply(1:nyears,
                       function(y) u + rGPD(n, xi=xi.true.B[y], beta=1)))
## build data frame
x <- data.frame(covar=covar, time=time, value=c(lossA, lossB))

## fit with gpd.fit
fit.coles <- gpd.fit(x$value, threshold=u, shl=1, sigl=1, ydat=x)
xi.fit.coles.A <- fit.coles$mle[3]+1*fit.coles$mle[4]
xi.fit.coles.B <- fit.coles$mle[3]+2*fit.coles$mle[4]

## fit with gamGPDfit()
fit <- gamGPDfit(x, threshold=u, datvar="value", xiFrhs=~covar, nuFrhs=~covar,
                 epsxi=eps, epsnu=eps)
xi.fit <- fitted(fit$xiObj)
xi.fit.A <- as.numeric(xi.fit[1]) # fit for group "A"
xi.fit.B <- as.numeric(xi.fit[nyears*n+1]) # fit for group "B"

## comparison
xi.fit.A-xi.fit.coles.A
xi.fit.B-xi.fit.coles.B

## End(Not run) # dontrun

ismev documentation built on May 1, 2019, 9:10 p.m.