I was looking through wiki's treatment on the title topic in https://en.wikipedia.org/wiki/Random_variable and am completely stumped on this particular section:

There are several specifics that elude me.

- How is the following progression derived

$$Y = log(1 + e^{-X}) Longrightarrow F_Y(y) = Pr( log(1+e^{-X}) le y )$$

and then the next step

$$Pr( log(1+e^{-X}) le y) = Pr( X ge -log(e^y – 1) )$$

Thx for filling in the blanks.

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#### Best Answer

**Introduction.** The so-called "CDF method" is one way to find the distribution of a the transformation $Y = g(X)$ of a random variable $X$ with a known CDF. Let's look at a simpler example first: Suppose $X sim mathsf{Univ}(0,1)$ and find the CDF of $Y = g(X) = sqrt{X}.$ The support of $X$ is $(0,1)$ and it is clear that the support of $Y$ will also be $(0,1).$

The CDF of $X$ is $F_X(x) = x,$ for $x in (0,1).$ Then the CDF of $Y$ is $$P(Y le y) = P(g(X) le y) = P(sqrt{X} le y) = P(X le y^2) = y^2,$$ for $y in (0,1).$ The last step uses $F_X(x) = P(X le x) = x,$ where $y^2 = x.$ Thus the PDF of $Y$ is $f_Y(y) = F_Y^prime(y) = dy^2/dy = 2y,$ which we recognize as the PDF of $mathsf{Beta}(2,1).$

Illustrating this with a random sample of $n = 10^5$ observations $X_i$ from $mathsf{Unif}(0,1),$ we have the following results (in R):

`set.seed(615) x = runif(10^5, 0, 1); y = sqrt(x) par(mfrow=c(1,2)) hist(x, prob=T, col="skyblue2", main="X ~ UNIF(0,1)") curve(dunif(x, 0, 1), add=T, n=10001, lwd=2, col="brown") hist(y, prob=T, col="skyblue2", main="Y ~ BETA(2,1)") curve(dbeta(x, 2, 1), add=T, n=10001, lwd=2, col="brown") par(mfrow=c(1,1)) `

**Your Question.** Now let's do a similar procedure for $X$ with CDF $F_X(x) = P(X le x) = (1 + e^{-x})^{-theta},$ for $theta > 0$ and the transformation $Y = g(X) = log(1+e^{-X}),$ which has support $(0, infty).$

Using the CDF method again, we have:

$$F_Y(y) = P(Yle y) = P(log(1 + e^{-X})le y) = P(1+e^{-X} le e^y)\ =P(e^{-X} le e^y – 1) = P(-X le log(e^y -1))\ = P(X ge -log(e^y -1)) = cdots,$$

So, $F_y(y) = 1-e^{-theta y},$ for $y > 0,$ as claimed.

We illustrate with a random sample of $n = 10^5$ observations from the original logistic distribution with $theta = 1.$ This distribution can be sampled in terms of standard uniform distributions as shown in the R code; see Wikipedia, second bullet under Related Distributions.

`set.seed(2019) u = runif(10^5); x = log(u) - log(1-u) y = log(1 + exp(-x)) par(mfrow=c(1,2)) hist(x, prob=T, br=30, ylim=c(0,.25), col="skyblue2", main="Logistic") curve(exp(-x)/(1+exp(-x))^2, add=T, lwd=2, col="brown") hist(y, prob=T, ylim=c(0,1), col="skyblue2", main="Exponential") curve(dexp(x,1), add=T, lwd=2, n=10001, col="brown") par(mfrow=c(1,1)) `

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