Transforming functions of random variables (exponential->Weibull)

In summary, the conversation discusses finding the density function of Y, which is defined as Y=X^(1/a) and X has an exponential distribution with parameter L. The resulting density function is determined to be La(s^[a-1])e^(-L[s^a)), which is a variation of the Weibull distribution.
  • #1
slaux89
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Homework Statement



Suppose X has an exponential with parameter L and Y=X^(1/a).
Find the density function of Y. This is the Weibull distribution

Homework Equations





The Attempt at a Solution



X~exponential (L) => fx(s)= Le^(-Ls)

Fx(s)=P(X<s) = 1-e^(-Ls)

P(Y<s)=P(X^(1/a)<s)=P(X<s^a)= 1-e^(-L[s^a])= Fy(s)

thus fy(s) = La(s^[a-1])e^(-L[s^a))

However, this doesn't seem to be the Weibull distribution. Did I do something wrong? Or is this just a variation of it?
 
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  • #2
Looks fine to me.
 
  • #3
Alright, thanks!
 

FAQ: Transforming functions of random variables (exponential->Weibull)

What is the difference between an exponential and a Weibull distribution?

An exponential distribution is a continuous probability distribution that measures the time between events in a Poisson process, while a Weibull distribution is a continuous probability distribution that measures the time to failure in a system. They have different shapes and parameters, with the Weibull distribution allowing for a wider range of shapes.

How do you transform a random variable from an exponential to a Weibull distribution?

To transform a random variable from an exponential to a Weibull distribution, you can use the inverse transform method. This involves using the inverse cumulative distribution function of the Weibull distribution to transform the random variable from the exponential distribution to the Weibull distribution.

What are some common applications of transforming exponential to Weibull distributions?

Transforming exponential to Weibull distributions is commonly used in reliability engineering, where the Weibull distribution is used to model the time to failure of a component or system. It can also be used in survival analysis, where the Weibull distribution can model the time until an event occurs, such as death or recovery from a disease.

Are there any limitations or assumptions when transforming exponential to Weibull distributions?

One limitation is that the Weibull distribution assumes that failures occur due to wear and tear, rather than sudden catastrophic events. Another assumption is that the failure rate of the system follows a constant or monotonically decreasing pattern over time. Additionally, the data used for the transformation should be independent and identically distributed.

How can I determine if a transformation from exponential to Weibull distributions is appropriate for my data?

You can use statistical tests, such as the Kolmogorov-Smirnov test or the Anderson-Darling test, to determine if the data follows a Weibull distribution. If the data does not follow a Weibull distribution, there are other distributional transformations that can be explored, such as lognormal or gamma distributions. It is also important to consider the underlying theory and assumptions of the data before deciding on a transformation.

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