jackFuncBlock {boodd} | R Documentation |
Jackknife Variance Function Using Blocks of Fixed Length
Description
Creates a vector-valued function for computing both the statistic defined by func
and the estimated jackknife variance based on the blocks.
Usage
jackFuncBlock(func, length.block = NULL, ...)
Arguments
func |
The function used to compute the statistic on each sample. |
length.block |
An integer; the block length.
If not provided, a default value equals |
... |
Optional additional arguments for the |
Details
The jackFuncBlock
function constructs a new function that,
when applied to a data sample,
calculates both the statistic specified by func
and its associated
jackknife variance based on non-overlapping blocks of fixed length equals to length.block
.
The block jackknife method is an extension of the jackknife resampling technique, used to estimate the bias and variance of a statistical estimate in the presence of dependent data.
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.
Carlstein, E. (1986). The use of subseries methods for estimating the variance of a general statistic from a stationary time series. Annals of Statist., 14, 1171-1179.
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
,
jackFunc
,
blockboot
,
jackVarBlock
,
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 <- arima.sim(list(order = c(1, 0, 4), ar = 0.5, ma = c(0.7, 0.4, -0.3, -0.1)), n = 100)
# Create a function to compute the empirical skewness and its variance based on blocks of size 5
jfb <- jackFuncBlock(func, length.block = 5)
# Bootstrapping of the skewness and its variance allows to construct
# bootstrap-t confidence intervals
boo2 <- blockboot(x, jfb, 299, length.block=7, method="circular")
confint(boo2, method="all")