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Let (chi ^{-2}_{d}) be the quantile function of the chi-squared distribution with d degrees of freedom.
Let Q G u = G −1 u) be the quantile function (qf) of G for 0
Aljarrah et al. (2014) considered the function W to be the quantile function of a random variable Y and defined the T-R{Y} family.
There are good reasons to let the random variable Y in the T-R{Y} framework be the quantile function of GLD.
One of the advantages for letting the random variable Y in the T-R{Y} framework to be the quantile function of GLD is that the generalized lambda random variable leads to three different types of support as shown in sub-section 2.2.
Aljarrah et al. (2014) took W (F x)) to be the quantile function of a random variable Y and defined the T - X {Y} family as G ( x ) = ∫ a Q Y ( F ( x ) ) r ( t ) dt = R ( Q Y ( F ( x ) ), (1.3).
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The next lemma makes a connection between the quantile function for the random variable X which follows the T-R{GL} families of distributions and the quantile functions of the random variables T and R. Lemma 2 (Quantiles): Let Q T (u) and Q R (u) be the quantile functions of the random variables T and R, respectively.
Also, for any joint d-dimensional distribution function F, let F i−1 be the quantile functions of the i-th margin, then the function C x1,…, x d) = F(F1−1 x1),…, F d−1(x d) is a d-variate copula.
Where ( {F_{tilde{c}}}^{-1}(w) ) is the quantile function for the distribution of ( tilde{C} ).
where Q Y (p) is the quantile function of the random variable Y.
The T-X(W) can be derived through the T-X{Y} framework by noting that the W function is the quantile function for the random variable Y.
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