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Multivariate Subgaussian Stable Density for matrix inputs

Source: R/dmvss_mat.R
dmvss_mat.Rd

Computes the the density function of the multivariate subgaussian stable distribution for arbitrary alpha, shape matrices, and location vectors. See Nolan (2013).

Usage

dmvss_mat(
  x,
  alpha = 1,
  Q = NULL,
  delta = rep(0, d),
  outermost.int = c("stats::integrate", "cubature::adaptIntegrate",
    "cubature::hcubature")[2],
  spherical = FALSE,
  subdivisions.si = 100L,
  rel.tol.si = .Machine$double.eps^0.25,
  abs.tol.si = rel.tol.si,
  stop.on.error.si = TRUE,
  tol.ai = 1e-05,
  fDim.ai = NULL,
  maxEval.ai = 0,
  absError.ai = 0,
  doChecking.ai = FALSE,
  which.stable = c("libstable4u", "stabledist")[1]
)

Arguments

x

nxd matrix of n variates of d-dimension

alpha

default to 1 (Cauchy). Must be 0<alpha<2

Q

Shape matrix. See Nolan (2013).

delta

location vector

outermost.int

select which integration function to use for outermost integral. Default is "stats::integrate" and one can specify the following options with the .si suffix. For diagonal Q, one can also specify "cubature::adaptIntegrate" and use the .ai suffix options below (currently there is a bug for non-diagnoal Q).

spherical

default is FALSE. If true, use the spherical transformation. Results will be identical to spherical = FALSE but may be faster.

subdivisions.si

the maximum number of subintervals. The suffix .si indicates a stats::integrate() option for the outermost semi-infinite integral in the product distribution formulation.

rel.tol.si

relative accuracy requested. The suffix .si indicates a stats::integrate() option for the outermost semi-infinite integral in the product distribution formulation.

abs.tol.si

absolute accuracy requested. The suffix .si indicates a stats::integrate() option for the outermost semi-infinite integral in the product distribution formulation.

stop.on.error.si

logical. If true (the default) an error stops the function. If false some errors will give a result with a warning in the message component. The suffix .si indicates a stats::integrate() option for the outermost semi-infinite integral in the product distribution formulation.

tol.ai

The maximum tolerance, default 1e-5. The suffix .ai indicates a cubature::adaptIntegrate type option for the outermost semi-infinite integral in the product distribution formulation.

fDim.ai

The dimension of the integrand, default 1, bears no relation to the dimension of the hypercube The suffix .ai indicates a cubature::adaptIntegrate type option for the outermost semi-infinite integral in the product distribution formulation.

maxEval.ai

The maximum number of function evaluations needed, default 0 implying no limit The suffix .ai indicates a cubature::adaptIntegrate type option for the outermost semi-infinite integral in the product distribution formulation.

absError.ai

The maximum absolute error tolerated The suffix .ai indicates a cubature::adaptIntegrate type option for the outermost semi-infinite integral in the product distribution formulation.

doChecking.ai

A flag to be thorough checking inputs to C routines. A FALSE value results in approximately 9 percent speed gain in our experiments. Your mileage will of course vary. Default value is FALSE. The suffix .ai indicates a cubature::adaptIntegrate type option for the outermost semi-infinite integral in the product distribution formulation.

which.stable

defaults to "libstable4u", other option is "stabledist". Indicates which package should provide the univariate stable distribution in this production distribution form of a univariate stable and multivariate normal.

Value

The object returned depends on what is selected for outermost.int. In the case of the default, stats::integrate, the value is a list of class "integrate" with components:

value

the final estimate of the integral.

abs.error

estimate of the modulus of the absolute error.

subdivisions

the number of subintervals produced in the subdivision process.

message

"OK" or a character string giving the error message.

call

the matched call.

Note: The reported abs.error is likely an under-estimate as integrate assumes the integrand was without error, which is not the case in this application.

References

Nolan, John P. "Multivariate elliptically contoured stable distributions: theory and estimation." Computational Statistics 28.5 (2013): 2067-2089.

Examples


## print("mvsubgaussPD (d=2, alpha=1.71):")
Q <- matrix(c(10,7.5,7.5,10),2)
mvpd::dmvss(x=c(0,1), alpha=1.71, Q=Q)
#> 0.01211828 with absolute error < 5.8e-05

## more accuracy = longer runtime
mvpd::dmvss(x=c(0,1),alpha=1.71, Q=Q, abs.tol=1e-8)
#> 0.01211828 with absolute error < 5.7e-07

Q <- matrix(c(10,7.5,7.5,7.5,10,7.5,7.5,7.5,10),3)
## print("mvsubgausPD (d=3, alpha=1.71):")
mvpd::dmvss(x=c(0,1,2), alpha=1.71, Q=Q)
#> 0.001602922 with absolute error < 4.4e-05
mvpd::dmvss(x=c(0,1,2), alpha=1.71, Q=Q, spherical=TRUE)
#> 0.001602922 with absolute error < 4.4e-05

## How `delta` works: same as centering
X <- c(1,1,1)
Q <- matrix(c(10,7.5,7.5,7.5,10,7.5,7.5,7.5,10),3)
D <- c(0.75, 0.65, -0.35)
mvpd::dmvss(X-D, alpha=1.71, Q=Q)
#> 0.001940025 with absolute error < 6e-05
mvpd::dmvss(X  , alpha=1.71, Q=Q, delta=D)
#> 0.001940025 with absolute error < 6e-05