Suppose that I have a random variable $X$ with the following PDF:
$$f_X(x)=\frac{(ln(x))^{\alpha-1}}{\Gamma(\alpha)\beta^\alpha x^{1+\frac{1}{\beta}}}$$ for $1<x<\infty$, $\alpha>0$, and $\beta>0$.
Suppose I want to calculate the mean and variance of this distribution. But I don't know how to compute the integral:
$$\int_{1}^{\infty}\frac{(ln(x))^{\alpha-1}}{\Gamma(\alpha)\beta^\alpha x^{\frac{1}{\beta}}}dx$$ for the first moment. Similarly, I don't know how to compute the integral for the second moment needed for the variance either. Can this definite integral be evaluated in terms of $\alpha$ and $\beta$? If so, how? If not, how else would you calculate the mean and variance?
We can compute the $k$-th moment this way:$$\int_{1}^{\infty}x^k\frac{(ln(x))^{\alpha-1}}{\Gamma(\alpha)\beta^\alpha x^{\frac{1}{\beta}+1}}dx = \frac{1}{\Gamma(\alpha)\beta^{\alpha}}\int_{0}^{\infty} \frac{e^{uk}u^{\alpha-1}}{e^{\frac {u}{\beta}}}du = \frac{1}{\Gamma(\alpha)\beta^{\alpha}(\frac{1}{\beta}-k)^{\alpha}}\int_{0}^{\infty} t^{\alpha-1}e^{-t}dt = \frac{\Gamma(\alpha)}{\Gamma(\alpha)\beta^{\alpha}(\frac{1}{\beta}-k)^{\alpha}} = (1-k\beta)^{-\alpha}.$$ Where we used these two transformations: $u=ln(x)$ and $t= u(\frac 1 {\beta}-k)$.