blockboot {boodd} | R Documentation |
Block Bootstrap
Description
The function applies block bootstrap methods to a time series.
This function allows the following block bootstrap methods to be used: the Moving Block Bootstrap (Kunsch (1989), Liu and Singh (1992)), the Nonoverlapping Block Bootstrap (Carlstein (1986)), the Circular Block Bootstrap (Politis and Romano (1992)), and the Stationary Bootstrap (Politis and Romano (1994)).
Usage
blockboot(
x,
func,
B,
length.block = NULL,
method = c("movingblock", "nonoverlapping", "circular", "stationary"),
moon = NULL,
replace = "TRUE",
...
)
Arguments
x |
A vector or a time series. |
func |
The function to apply to each sample. |
B |
A positive integer; the number of bootstrap replications. |
length.block |
A positive integer; the length of the blocks. |
method |
The block bootstrap method. The possible values of the |
moon |
Integer or |
replace |
Logical. If |
... |
Optional additional arguments for the |
Details
Nonoverlapping Block Bootstrap (NBB) consists in cutting the original time
series into nonoverlapping blocks of fixed length length.block
and in
resampling these blocks to reconstruct a
bootstrap time series.
Moving Block Bootstrap (MBB) consists in drawing independently overlapping blocks
of fixed size length.block
to reconstruct a bootstrap time series of
length of the original process.
Circular Block Bootstrap (CBB) consists in wrapping the data on a circle and to
create the corresponding overlapping blocks, so that each value of the time series
appears globally the same number of times in all the blocks. This generally reduce the
bias of the bootstrap distribution.
Stationary Bootstrap (SB) is based on blocks with random length, which ensure that
the bootstrap sample is stationary.
Value
An object of class boodd
.
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.
Künsch, H. (1989). The jackknife and the bootstrap for general stationary observations. Ann. Statist., 17, 1217-1241.
Liu, R. and Singh, K. (1992). Moving block jackknife and bootstrap capture weak dependence. Exploring the Limits of Bootstrap., Series in Probab. Math. Statist. Wiley, New York, pp 225-248.
Politis, D.N. and Romano, J.P. (1994). The stationary bootstrap. J. Amer. Statist. Assoc., 89, 1303–1313.
Politis, D.N. and Romano, J.P. (1992). A circular block-resampling procedure for stationary data. Exploring the Limits of Bootstrap., Series in Probab. Math. Statist. Wiley, New York, pp 263-270.
See Also
boots
,
bootsemi
,
plot.boodd
,
confint.boodd
,
fieldboot
,
jackVarBlock
.
Examples
B <- 999
data(airquality)
x <- airquality$Wind
n <- length(x)
b <- floor(sqrt(n))
boo1 <- blockboot(x,mean,B,b,method="moving")
plot(boo1,main="MBB", nclass=30)
confint(boo1, method="all")