NNS.MC: NNS Monte Carlo Sampling

In NNS: Nonlinear Nonparametric Statistics

NNS Monte Carlo Sampling

Description

Monte Carlo sampling from the maximum entropy bootstrap routine NNS.meboot, ensuring the replicates are sampled from the full [-1,1] correlation space.

Usage

NNS.MC(
  x,
  reps = 30,
  lower_rho = -1,
  upper_rho = 1,
  by = 0.01,
  exp = 1,
  type = "spearman",
  drift = TRUE,
  xmin = NULL,
  xmax = NULL,
  ...
)

Arguments

x	vector of data.
reps	numeric; number of replicates to generate, 30 default.
lower_rho	numeric [-1,1]; .01 default will set the from argument in seq(from, to, by).
upper_rho	numeric [-1,1]; .01 default will set the to argument in seq(from, to, by).
by	numeric; .01 default will set the by argument in seq(-1, 1, step).
exp	numeric; 1 default will exponentially weight maximum rho value if exp > 1. Shrinks values towards upper_rho.
type	options("spearman", "pearson", "NNScor", "NNSdep"); type = "spearman"(default) dependence metric desired.
drift	logical; TRUE default preserves the drift of the original series.
xmin	numeric; the lower limit for the left tail.
xmax	numeric; the upper limit for the right tail.
...	possible additional arguments to be passed to NNS.meboot.

Value

• ensemble average observation over all replicates as a vector.

• replicates maximum entropy bootstrap replicates as a list for each rho.

References

Vinod, H.D. and Viole, F. (2020) Arbitrary Spearman's Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations https://www.ssrn.com/abstract=3621614

Examples

## Not run: 
# To generate a set of MC sampled time-series to AirPassengers
MC_samples <- NNS.MC(AirPassengers, xmin = 0)

## End(Not run)

NNS documentation built on Nov. 28, 2023, 1:10 a.m.