Transformation of random variable

In summary, the conversation discusses a discrete random variable X with values x1,...,xn and probabilities of 1/n. The question is raised about the probability function of Y, where Y is a function of X. A member suggests the probability mass function of Y is f_Y(y)=|g^-1(y)|/n and may be simplified with more information about g.
  • #1
WMDhamnekar
MHB
376
28
Hello,

A discrete random variable X takes values $x_1,...,x_n$ each with probability $\frac1n$. Let Y=g(X) where g is an arbitrary real-valued function. I want to express the probability function of Y(pY(y)=P{Y=y}) in terms of g and the $x_i$
How can I answer this question?

If any member knows the correct answer, he/she may reply with correct answer.
 
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  • #2
The notation Y(pY(y)=P{Y=y}) is confusing. For one, $Y$ accepts as argument elements of $\{x_1,\ldots.x_n\}$ and not equalities. if you need the probability mass function of $Y$, it is \(\displaystyle f_Y(y)=|g^{-1}(y)|/n\). I don't think this can be simplified unless we know more about $g$.
 

Related to Transformation of random variable

What is the transformation of a random variable?

The transformation of a random variable is the process of converting a random variable from one form to another, while preserving its underlying distribution. This is often done to make the data more easily interpretable or to meet certain assumptions for statistical analysis.

Why is transformation of random variable important in statistics?

Transformation of random variable is important in statistics because it can help to improve the distributional properties of the data, making it easier to apply statistical tests and models. It can also help to reduce the impact of outliers and improve the accuracy of statistical inference.

What are some common types of transformations for random variables?

Some common types of transformations for random variables include logarithmic, exponential, square root, and power transformations. These transformations are often used to achieve a more symmetrical distribution or to stabilize the variance of the data.

How do you know which type of transformation to use for a specific random variable?

The type of transformation to use for a specific random variable depends on the distribution and properties of the data. It is important to visually inspect the data and choose a transformation that results in a more normal distribution and equal variance. Additionally, statistical tests and diagnostic plots can help in determining the appropriate transformation.

Can transformation of a random variable change the interpretation of the data?

Yes, transformation of a random variable can change the interpretation of the data. For example, a logarithmic transformation can change the interpretation from an additive relationship to a multiplicative one. It is important to consider the implications of the transformation on the interpretation of the data and to communicate this clearly in any analysis or reporting.

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