ddst.againststochdom.test | R Documentation |
Performs data driven smooth non-parametric two-sample test against one-sided alternatives (stochastic dominance). Suppose that we have random samples from two distributions F and G. The null hypothesis is that F(x) < G(x) for some x while the alternative is that at F(x) >= G(x) for all x with strict inequality for at least one x. Detailed description of the test statistic is provided in Ledwina and Wylupek (2012).
ddst.againststochdom.test(
x,
y,
k.N = 4,
alpha = 0.05,
t,
nr = 1e+05,
compute.cv = FALSE
)
x |
a (non-empty) numeric vector of data |
y |
a (non-empty) numeric vector of data |
k.N |
an integer specifying a level of complexity of the grid considered, only for advanced users |
alpha |
a significance level |
t |
an alpha-dependent tunning parameter in the penalty in the model selection rule |
nr |
an integer specifying the number of runs for a p-value and a critical value computation if any |
compute.cv |
a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not |
Two-sample test against one-sided alternatives. Ledwina and Wylupek (2012). https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9469.2011.00787.x
set.seed(7)
# H0 is true
x <- runif(80)
y <- runif(80)
t <- ddst.againststochdom.test(x, y, alpha = 0.05, t = 2.2, k.N = 4)
t
plot(t)
# H0 is false
# known fixed alternative
x <- runif(80)
y <- rbeta(80,4,2)
t <- ddst.againststochdom.test(x, y, alpha = 0.05, t = 2.2, k.N = 4)
t
plot(t)
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