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The first one corresponds to a comparison between the quantile functions of the citation distributions.
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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.
We use B-splines to approximate the quantile functions as τ varies and consider the composite quantile regression to estimate the parameters.
In the literature, some of the quantile functions do not have closed form expressions.
The quantile functions of these random variables can be used to generate new T-X{Y} families.
In general, the quantile function enables one to find the relationship between one random variable and another random variable.
We derive a power series for the quantile function.
The quantile function of a random variable is the inverse function of its distribution function.
Many other W functions can be defined by using the quantile function approach.
By using the quantile function approach, let λ = F x), then W = - log (1 - λ) is the quantile function of standard exponential distribution.
The quantile function X* =Q(U ,0
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