| Run_KMT | R Documentation |
Performs goodness-of-fit test through Khmaladze matringale transformation
Run_KMT(
X,
strDistr = "Normal",
bEstimation = FALSE,
bFast_Estimation = FALSE,
bParallel = FALSE,
nThreads = 16
)
X |
a random sample of n observations |
strDistr |
a null distribution for the hypothesis test: Normal, Cauchy, Logistic, or Gumbel. |
bEstimation |
a logical value which specifies whether or not to estimate parameters. The default value is TRUE. For FALSE, ( |
bFast_Estimation |
a logical value which specifies whether or not to use the maximum likelihood estimator ( |
bParallel |
a logical value which specifies whether or not to use the parallel computing. The default value is FALSE. |
nThreads |
the number of threads when bParallel is TRUE. The default value is 16. |
A list of the following values:
opt.x is the value of x where the optimum of the objective function - which is also the test statistic - occurs.
test.stat is the test statistic obtained through Khmaladze martingale transformation.
the point estimate for the location parameter mu
the point estimate for the scale parameter sigma
[1] Khmaladze, E.V., Koul, H.L. (2004). Martingale transforms goodness-of-fit tests in regression models. Ann. Statist., 32. 995-1034
[2] E.V. Khmaladze, H.L. Koul (2009). Goodness-of-fit problem for errors in nonparametric regression: distribution free approach. Ann. Statist., 37(6A) 3165-3185.
[3] Kim, Jiwoong (2020). Implementation of a goodness-of-fit test through Khmaladze martingale transformation. Comp. Stat., 35(4): 1993-2017
####################
n=20
mu0=2; sigma0=1
X = rnorm(n, mu0, sigma0)
Run_KMT(X, strDistr="Normal")
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