jackFunc {boodd} | R Documentation |
Jackknife Variance Function
Description
Creates a vector-valued function for computing both the statistic defined by func
and the estimated jackknife variance of the statistic.
Usage
jackFunc(func, ...)
Arguments
func |
The function used to compute the statistic on each sample. |
... |
Optional additional arguments for the |
Details
The jackFunc
function constructs a new function that, when applied to a data sample,
calculates both the statistic specified by func
and its associated jackknife variance.
This newly created function is useful in generic bootstrap procedures, particularly
for constructing bootstrap-t confidence intervals.
Value
Returns an object, which is a function.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted.
Efron, B. (1979). Bootstrap methods: another look at the jackknife. Ann. Statist., 7, 1-26.
Gray, H., Schucany, W. and Watkins, T. (1972). The Generalized Jackknife Statistics. Marcel Dekker, New York.
Quenouille, M.H. (1949). Approximate tests of correlation in time-series. J. Roy. Statist. Soc., Ser. B, 11, 68-84.
See Also
jackVar
,
boots
,
jackVarBlock
,
jackFuncBlock
,
jackFuncRegen
.
Examples
# Create a function to compute the empirical skewness
func <- function(x) { mean((x - mean(x))^3) / (mean((x - mean(x))^2)^(3/2)) }
x <- rnorm(100)
# Create a function to compute the empirical skewness and its variance
jf <- jackFunc(func)
# Bootstrapping of the skewness and its variance allows to construct
# bootstrap-t confidence intervals
boo1 <- boots(x, jf, 299)
confint(boo1, method="all")