varianceReduction: Variance-Reduction Methods

In copula: Multivariate Dependence with Copulas

Variance-Reduction Methods

Description

Computing antithetic variates or Latin hypercube samples.

Usage

rAntitheticVariates(u)
rLatinHypercube(u, ...)

Arguments

u	a n\times d-matrix (or d-vector) of random variates in the unit hypercube.
...	additional arguments passed to the underlying rank().

Details

rAntitheticVariates() takes any copula sample u, builds 1-u, and returns the two matrices in the form of an array; this can be used for the variance-reduction method of (componentwise) antithetic variates.

rLatinHypercube() takes any copula sample, shifts its marginal ranks minus 1 by standard uniforms and then divides by the sample size in order to obtain a Latin hypercubed sample.

Value

rAntitheticVariates()

array of dimension n\times d\times 2, say r, where r[,,1] contains the original sample u and r[,,2] contains the sample 1-u.

rLatinHypercube()

matrix of the same dimensions as u.

References

Cambou, M., Hofert, M. and Lemieux, C. (2016). Quasi-random numbers for copula models. Statistics and Computing, 1–23.

Packham, N. and Schmidt, W. M. (2010). Latin hypercube sampling with dependence and applications in finance. Journal of Computational Finance 13(3), 81–111.

Examples

### 1 Basic plots ##############################################################

## Generate data from a Gumbel copula
cop <- gumbelCopula(iTau(gumbelCopula(), tau = 0.5))
n <- 1e4
set.seed(271)
U <- rCopula(n, copula = cop)

## Transform the sample to a Latin Hypercube sample
U.LH <- rLatinHypercube(U)

## Plot
## Note: The 'variance-reducing property' is barely visible, but that's okay
layout(rbind(1:2))
plot(U,    xlab = quote(U[1]), ylab = quote(U[2]), pch = ".", main = "U")
plot(U.LH, xlab = quote(U[1]), ylab = quote(U[2]), pch = ".", main = "U.LH")
layout(1) # reset layout

## Transform the sample to an Antithetic variate sample
U.AV <- rAntitheticVariates(U)
stopifnot(identical(U.AV[,,1],  U ),
          identical(U.AV[,,2], 1-U))

## Plot original sample and its corresponding (componentwise) antithetic variates
layout(rbind(1:2))
plot(U.AV[,,1], xlab = quote(U[1]), ylab = quote(U[2]), pch=".", main= "U")
plot(U.AV[,,2], xlab = quote(U[1]), ylab = quote(U[2]), pch=".", main= "1 - U")
layout(1) # reset layout


### 2 Small variance-reduction study for exceedance probabilities ##############

## Auxiliary function for approximately computing P(U_1 > u_1,..., U_d > u_d)
## by Monte Carlo simulation based on pseudo-random numbers, Latin hypercube
## sampling and quasi-random numbers.
sProb <- function(n, copula, u)
{
    d <- length(u)
    stopifnot(n >= 1, inherits(copula, "Copula"), 0 < u, u < 1,
              d == dim(copula))
    umat <- rep(u, each = n)
    ## Pseudo-random numbers
    U <- rCopula(n, copula = copula)
    PRNG <- mean(rowSums(U > umat) == d)
    ## Latin hypercube sampling (based on the recycled 'U')
    U. <- rLatinHypercube(U)
    LHS <- mean(rowSums(U. > umat) == d)
    ## Quasi-random numbers
    U.. <- cCopula(sobol(n, d = d, randomize = TRUE), copula = copula,
                   inverse = TRUE)
    QRNG <- mean(rowSums(U.. > umat) == d)
    ## Return
    c(PRNG = PRNG, LHS = LHS, QRNG = QRNG)
}

## Simulate the probabilities of falling in (u_1,1] x ... x (u_d,1]
library(qrng) # for quasi-random numbers
(Xtras <- copula:::doExtras()) # determine whether examples will be extra (long)
B <- if(Xtras)  500 else 100 # number of replications
n <- if(Xtras) 1000 else 200 # sample size
d <- 2 # dimension; note: for d > 2, the true value depends on the seed
nu <- 3 # degrees of freedom
th <- iTau(tCopula(df = nu), tau = 0.5) # correlation parameter
cop <- tCopula(param = th, dim = d, df = nu) # t copula
u <- rep(0.99, d) # lower-left endpoint of the considered cube
set.seed(42) # for reproducibility
true <- prob(cop, l = u, u = rep(1, d)) # true exceedance probability
system.time(res <- replicate(B, sProb(n, copula = cop, u = u)))

## "abbreviations":
PRNG <- res["PRNG",]
LHS  <- res["LHS" ,]
QRNG <- res["QRNG",]

## Compute the variance-reduction factors and % improvements
vrf  <- var(PRNG) / var(LHS)                    # variance reduction factor w.r.t. LHS
vrf. <- var(PRNG) / var(QRNG)                   # variance reduction factor w.r.t. QRNG
pim  <- (var(PRNG) - var(LHS)) / var(PRNG) *100 # improvement w.r.t. LHS
pim. <- (var(PRNG) - var(QRNG))/ var(PRNG) *100 # improvement w.r.t. QRNG

## Boxplot
boxplot(list(PRNG = PRNG, LHS = LHS, QRNG = QRNG), notch = TRUE,
        main = substitute("Simulated exceedance probabilities" ~
                              P(bold(U) > bold(u))~~ "for a" ~ t[nu.]~"copula",
                          list(nu. = nu)),
        sub = sprintf(
           "Variance-reduction factors and %% improvements: %.1f (%.0f%%), %.1f (%.0f%%)",
            vrf, pim, vrf., pim.))
abline(h = true, lty = 3) # true value
mtext(sprintf("B = %d replications with n = %d and d = %d", B, n, d), side = 3)

copula documentation built on Sept. 11, 2024, 7:48 p.m.