qnvmix: Quantile Function of a univariate Normal Variance Mixture...

In nvmix: Multivariate Normal Variance Mixtures

Quantile Function of a univariate Normal Variance Mixture Distribution

Description

Evaluating multivariate normal variance mixture distribution functions (including normal and Student t for non-integer degrees of freedom).

Usage

qnvmix(u, qmix, control = list(),
       verbose = TRUE, q.only = TRUE, stored.values = NULL, ...)

Arguments

u	vector of probabilities .
qmix	specification of the mixing variable W; see pnvmix() for details and examples.
control	list specifying algorithm specific parameters; see get_set_param().
verbose	logical, if TRUE a warning is printed if one of the error tolerances is not met.
q.only	logical. If TRUE, only the quantiles are returned; if FALSE, see Section 'value' below.
stored.values	matrix with 3 columns of the form [x, F(x), logf(x)] where F() and logf() are the distribution- and log-density function of the distribution specified in qmix. If provided it is used to determine starting values for internal newton proceudures. Only very basic checking is done.
...	additional arguments containing parameters of mixing distributions when qmix is a character string.

Details

This function uses a Newton procedure to estimate the quantile of the specified univariate normal variance mixture distribution. Internally, a randomized quasi-Monte Carlo (RQMC) approach is used to estimate the distribution and (log)density function; the method is similar to the one in pnvmix() and dnvmix(). The result depends slightly on .random.seed.

Internally, symmetry is used for u \le 0.5. Function values (i.e., df and log-density values) are stored and reused to get good starting values. These values are returned if q.only = FALSE and can be re-used by passing it to qnvmix() via the argument stored.values; this can significantly reduce run-time.

Accuracy and run-time depend on both the magnitude of u and on how heavy the tail of the underlying distributions is. Numerical instabilities can occur for values of u close to 0 or 1, especially when the tail of the distribution is heavy.

If q.only = FALSE the log-density values of the underlying distribution evaluated at the estimated quantiles are returned as well: This can be useful for copula density evaluations where both quantities are needed.

Underlying algorithm specific parameters can be changed via the control argument, see get_set_param() for details.

Value

If q.only = TRUE a vector of the same length as u with entries q_i where q_i satisfies q_i = inf_x { F(x) \ge u_i} where F(x) the univariate df of the normal variance mixture specified via qmix;

if q.only = FALSE a list of four:

$q:

Vector of quantiles,

$log.density:

vector log-density values at q,

$computed.values:

matrix with 3 columns [x, F(x), logf(x)]; see details above,

$newton.iterations:

vector giving the number of Newton iterations needed for u[i].

Author(s)

Erik Hintz, Marius Hofert and Christiane Lemieux

References

Hintz, E., Hofert, M. and Lemieux, C. (2021), Normal variance mixtures: Distribution, density and parameter estimation. Computational Statistics and Data Analysis 157C, 107175.

Hintz, E., Hofert, M. and Lemieux, C. (2022), Multivariate Normal Variance Mixtures in R: The R Package nvmix. Journal of Statistical Software, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v102.i02")}.

McNeil, A. J., Frey, R., and Embrechts, P. (2015). Quantitative Risk Management: Concepts, Techniques, Tools. Princeton University Press.

See Also

dnvmix(), rnvmix(), pnvmix()

Examples

## Evaluation points
u <- seq(from = 0.05, to = 0.95, by = 0.025)
set.seed(271) # for reproducibility

## Evaluate the t_{1.4} quantile function
df <- 1.4
qmix. <- function(u) 1/qgamma(1-u, shape = df/2, rate = df/2)
## If qmix = "inverse.gamma", qt() is being called
qt1 <- qnvmix(u, qmix = "inverse.gamma", df = df)
## Estimate quantiles (without using qt())
qt1. <- qnvmix(u, qmix = qmix., q.only = FALSE)
stopifnot(all.equal(qt1, qt1.$q, tolerance = 2.5e-3))
## Look at absolute error:
abs.error <- abs(qt1 - qt1.$q)
plot(u, abs.error, type = "l", xlab = "u", ylab = "Absolute error")
## Now do this again but provide qt1.$stored.values, in which case at most
## one Newton iteration will be needed:
qt2 <- qnvmix(u, qmix = qmix., stored.values = qt1.$computed.values, q.only = FALSE)
stopifnot(max(qt2$newton.iterations) <= 1)

nvmix documentation built on March 19, 2024, 3:07 a.m.