Bootstrap Distribution for Fitted Model

Description

This function allows the user to obtain draws from the (parametric) bootstrap distribution of the fitted model's parameters.

Usage

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boot.mle(model, B = 200, seed = NULL, start = NULL,
 method = "Nelder-Mead")

Arguments

model

mle object corresponding to the fitted model.

B

Requested number of bootstrap samples.

seed

A seed may be specified (see set.seed)

start

Starting values for the optimization algorithm (if is.null(start)==TRUE, the fitted model's parameters are used as starting values).

method

The optimization method to be used (see optim and mle).

Details

Parametric bootstrap – see References.

Value

model

mle object corresponding to the fitted model.

B

Requested number of bootstrap samples.

seed

The specified seed (see set.seed)

par.star

Array containing realized values from the bootstrap distribution of the maximum likelihood parameter estimators.

gof

The bootstrap distributions of two goodness-of-fit statistics: Anderson-Darling statistic and Pearson's correlation coefficient for the pair ("observed quantiles","fitted quantiles").

p.value

Bootstrap p-values for the two goodness-of-fit statistics.

failure.rate

The proportion of bootstrap samples for which optimization failed using the specified starting values.

total.time

The total amount of time required to generate B bootstrap samples.

References

Davison, A.C., and Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

See Also

mle, Q.conf.int, Q.boot.ci

Examples

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data(yarns)
x <- yarns$x
fit.x <- mle(x,'weibull',c(.1,.1))
boot.x <- boot.mle(fit.x,B=10)
boot.x$par.star
boot.x$p.value