Distribution of Log of Random Variable

In summary, the distribution of Y is a gaussian with mean rln(u) and variance r^2s^2. This can be derived by making a change of variables from X to Y and noting that the lower limit of the integral changes from 0 to -∞, resulting in a probability density function for Y that is only valid for values greater than or equal to 0. Therefore, the distribution of Y is not defined for negative values of X.
  • #1
andrewcheong
9
0
Let X and Y be random variables.

X ~ N(u,s^2)
Y = r ln X, where r is a constant.

What is the distribution of Y?

(This is not a homework problem. It's just related to something I was curious about, and I can't figure out how to solve this, if it is solvable...)
 
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  • #2
You know that

[tex] 1 = \int_{-\infty}^{\infty}dx~\frac{1}{\sqrt{2\pi \sigma^2}} \exp\left[\left(\frac{x-\mu}{\sigma}\right)^2\right] = \int_{0}^{\infty}dx~\frac{2}{\sqrt{2\pi \sigma^2}} \exp\left[\left(\frac{x-\mu}{\sigma}\right)^2\right] [/tex]

So, make a change of variables [itex]y = r \ln x[/itex]. The lower limit x = 0 becomes y = -\infty and the upper limit remains infinity. [itex]dy = r dx/x = r dx e^{-y/r}[/itex]

Hence,

[tex] 1 = \int_{-\infty}^{\infty}dy~\frac{2e^{y/r}}{r\sqrt{2\pi \sigma^2}} \exp\left[\left(\frac{e^{y/r}-\mu}{\sigma}\right)^2\right][/tex]

The integrand is thus the probability density function for y. Note that the distribution is only valid for values of x zero or greater, as y is not defined for x < 0. This is why in the first line I used the evenness of the gaussian integrand to write it in terms of x > 0 only.
 

1. What is the distribution of log of a random variable?

The distribution of log of a random variable refers to the probability distribution of the logarithm of a random variable. This distribution is often used in statistics and probability theory to model data that is positively skewed, meaning the majority of the data falls to the left of the mean.

2. How is the distribution of log of a random variable calculated?

The distribution of log of a random variable is typically calculated by taking the natural logarithm of each value in the dataset and then fitting a probability distribution to the resulting data. Some common distributions used for this purpose include the normal, log-normal, and Weibull distributions.

3. What is the purpose of using the distribution of log of a random variable?

The distribution of log of a random variable is often used to transform data that is not normally distributed into a more symmetrical distribution. This can make it easier to apply statistical techniques that assume a normal distribution, as well as improve the accuracy of certain statistical models.

4. Can the distribution of log of a random variable be used for any type of data?

No, the distribution of log of a random variable is typically only used for data that is positively skewed. It is not suitable for data that is negatively skewed or symmetrically distributed.

5. Are there any limitations to using the distribution of log of a random variable?

Yes, there are some limitations to using the distribution of log of a random variable. For example, this transformation may not be appropriate for data that contains extreme outliers. Additionally, the log transformation can sometimes lead to the loss of valuable information in the data.

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