Bound_EE1 {BoundEdgeworth} | R Documentation |
Uniform bound on Edgeworth expansion
Description
This function computes a non-asymptotically uniform bound on the difference between the cdf of a normalized sum of random variables and its 1st order Edgeworth expansion. It returns a valid value \(\delta_n\) such that \[ \sup_{x \in \mathbb{R}} \left| \textrm{Prob}(S_n \leq x) - \Phi(x) - \frac{\lambda_{3,n}}{6\sqrt{n}}(1-x^2) \varphi(x) \right| \leq \delta_n,\] where \(X_1, \dots, X_n\) be \(n\) independent centered variables, and \(S_n\) be their normalized sum, in the sense that \(S_n := \sum_{i=1}^n X_i / \textrm{sd}(\sum_{i=1}^n X_i)\). Here \(\lambda_{3,n}\) denotes the average skewness of the variables \(X_1, \dots, X_n\).
Usage
Bound_EE1(
setup = list(continuity = FALSE, iid = FALSE, no_skewness = FALSE),
n,
K4 = 9,
K3 = NULL,
lambda3 = NULL,
K3tilde = NULL,
regularity = list(C0 = 1, p = 2),
eps = 0.1,
verbose = 0
)
Arguments
setup |
logical vector of size 3 made up of the following components:
|
n |
sample size ( = number of random variables that appear in the sum). |
K4 |
bound on the 4th normalized moment of the random variables. We advise to use K4 = 9 as a general case which covers most “usual” distributions. |
K3 |
bound on the 3rd normalized moment.
If not given, an upper bound on |
lambda3 |
(average) skewness of the variables.
If not given, an upper bound on \(abs(lambda3)\)
will be derived from the value of |
K3tilde |
value of
\[
K_{3,n} + \frac{1}{n}\sum_{i=1}^n
\mathbb{E}|X_i| \sigma_{X_i}^2 / \overline{B}_n^3\]
where \(\overline{B}_n := \sqrt{(1/n) \sum_{i=1}^n E[X_i^2]}\).
If not given, an upper bound on |
regularity |
list of length up to 3
(only used in the
|
eps |
a value between 0 and 1/3 on which several terms depends.
Any value of |
verbose |
if it is |
Details
Note that the variables \(X_1, \dots, X_n\) must be independent
but may have different distributions (if setup$iid = FALSE
).
Value
A vector of the same size as n
with values \(\delta_n\)
such that
\[
\sup_{x \in \mathbb{R}}
\left| \textrm{Prob}(S_n \leq x) - \Phi(x)
- \frac{\lambda_{3,n}}{6\sqrt{n}}(1-x^2) \varphi(x) \right|
\leq \delta_n.\]
References
Derumigny A., Girard L., and Guyonvarch Y. (2021). Explicit non-asymptotic bounds for the distance to the first-order Edgeworth expansion, ArXiv preprint arxiv:2101.05780.
See Also
Bound_BE()
for a Berry-Esseen bound.
Gauss_test_powerAnalysis()
for a power analysis of the classical
Gauss test that is uniformly valid based on this bound on the Edgeworth
expansion.
Examples
setup = list(continuity = TRUE, iid = FALSE, no_skewness = TRUE)
regularity = list(C0 = 1, p = 2)
computedBound <- Bound_EE1(
setup = setup, n = c(150, 2000), K4 = 9,
regularity = regularity, eps = 0.1 )
setup = list(continuity = TRUE, iid = TRUE, no_skewness = TRUE)
regularity = list(kappa = 0.99)
computedBound2 <- Bound_EE1(
setup = setup, n = c(150, 2000), K4 = 9,
regularity = regularity, eps = 0.1 )
setup = list(continuity = FALSE, iid = FALSE, no_skewness = TRUE)
computedBound3 <- Bound_EE1(
setup = setup, n = c(150, 2000), K4 = 9, eps = 0.1 )
setup = list(continuity = FALSE, iid = TRUE, no_skewness = TRUE)
computedBound4 <- Bound_EE1(
setup = setup, n = c(150, 2000), K4 = 9, eps = 0.1 )
print(computedBound)
print(computedBound2)
print(computedBound3)
print(computedBound4)