cocoBoot function

Bootstrap Based Model Assessment Procedure

Bootstrap Based Model Assessment Procedure

Model checking procedure emphasizing reproducibility in fitted models to provide an overall evaluation of fit as proposed by Tsay (1992).

cocoBoot( coco, numb.lags = 21, rep.Bootstrap = 1000, conf.alpha = 0.05, julia = FALSE, julia_seed = NULL )

Arguments

  • coco: An object of class coco
  • numb.lags: Number of lags for which to compute autocorrelations
  • rep.Bootstrap: Number of bootstrap replicates to use
  • conf.alpha: Confidence level for the quantile intervals
  • julia: if TRUE, the bootstrap is run with Julia.
  • julia_seed: Seed for the julia implementation. Only used if julia equals TRUE.

Returns

an object of class cocoBoot. It contains the bootstraped confidence intervals of the autocorrelations and information on the model specifications.

Details

Computes bootstrap confidence intervals for the autocorrelations of a fitted model.

Examples

lambda <- 1 alpha <- 0.4 set.seed(12345) data <- cocoSim(order = 1, type = "Poisson", par = c(lambda, alpha), length = 100) fit <- cocoReg(order = 1, type = "Poisson", data = data) #assessment using bootstrap - R implementation boot_r <- cocoBoot(fit, rep.Bootstrap=400)

References

Tsay, R. S. (1992) Model checking via parametric bootstraps in time series analysis. Applied Statistics 41 , 1--15.

  • Maintainer: Manuel Huth
  • License: MIT + file LICENSE
  • Last published: 2023-10-01

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