sn-parametric-bootstrap: Parametric Bootstrap Goodness-of-Fit Tests for a Skew-Normal...

In PBGoF: Parametric Bootstrap Tests for the Skew-Normal Distribution

Parametric Bootstrap Goodness-of-Fit Tests for a Skew-Normal Distribution

Description

Tests skew-normal goodness of fit using a parametric bootstrap, re-estimating the model in each bootstrap sample with sn.fit.robust().

Usage

sn.para.bootstrap.ks.test(data = NULL, B = 1000L, seed = 103L,
  verbose = FALSE)

sn.para.bootstrap.cvm.test(data = NULL, B = 1000L, seed = 103L,
  verbose = FALSE)

Arguments

data	A numeric vector containing at least 10 finite observations.
B	A positive integer giving the number of bootstrap samples.
seed	A single integer, or NULL to use the current random-number stream. The caller's random-number state is restored on exit.
verbose	Logical; whether to print a short summary.

Details

The KS function uses \sqrt{n}D; the CvM function uses the Cramér–von Mises statistic. Failed fits are excluded and the p-value uses the finite-simulation correction (1 + \sum I(T_b >= T_0))/(B_{valid}+1). The names ending in .1 are compatibility aliases.

Value

A single numeric bootstrap p-value with attributes statistic, B, valid, and failed.

Examples

set.seed(123)
x <- sn::rsn(50, xi = 0, omega = 1, alpha = 3)
sn.para.bootstrap.ks.test(x, B = 99)
sn.para.bootstrap.cvm.test(x, B = 99)

PBGoF documentation built on Oct. 2, 2026, 5:09 p.m.