Description Usage Arguments Details Value References Examples
Simulates multiple realizations of the CUSUM statistic.
1 2 3 |
size |
Number of realizations to simulate |
kn |
A function returning a positive integer that is used in the definition of the trimmed CUSUSM statistic effectively setting the bounds over which the maximum is taken |
tau |
The weighting parameter for the weighted CUSUM statistic (defaults to zero for no weighting) |
use_kernel_var |
Set to |
kernel |
If character, the identifier of the kernel function as used in
the cointReg (see documentation for
|
bandwidth |
If character, the identifier of how to compute the bandwidth
as defined in the cointReg package (see
documentation for |
n |
The sample size for each realization |
gen_func |
The function generating the random sample from which the statistic is computed |
args |
A list of arguments to be passed to |
parallel |
Whether to use the foreach and doParallel packages to parallelize simulation (which needs to be initialized in the global namespace before use) |
This differs from sim_Vn()
in that the long-run variance is estimated
with this function, while sim_Vn()
assumes the long-run variance is
known. Estimation can be done in a variety of ways. If use_kernel_var
is set to TRUE
, long-run variance estimation using kernel-based
techniques will be employed; otherwise, a technique resembling standard
variance estimation will be employed. Any technique employed, though, will
account for the potential break points, as described in
\insertCitehorvathricemiller19;textualCPAT. See the documentation for
stat_Vn
for more details.
The parameters kernel
and bandwidth
control parameters for
long-run variance estimation using kernel methods. These parameters will be
passed directly to stat_Vn
.
Versions of the CUSUM statistic, such as the weighted or trimmed statistics,
can be simulated with the function by passing values to kn
and
tau
; again, see the documentation for stat_Vn
.
A vector of simulated realizations of the CUSUM statistic
1 2 3 4 | CPAT:::sim_Vn_stat(100)
CPAT:::sim_Vn_stat(100, kn = function(n) {floor(0.1 * n)}, tau = 1/3,
use_kernel_var = TRUE, gen_func = CPAT:::rchangepoint,
args = list(changepoint = 250, mean2 = 1))
|
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