Question involving pdf of change of variable

In summary, we found the probability density function of Y, which is the smallest of a sample of n observations on X, and it is given by fY (y) = n * k * exp(-y/μ) * (1 - exp(-y/μ))^(n−1) for θ ≤ y < ∞.
  • #1
alex07966
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A Geiger counter records the times at which radioactive particles are detected. However, if two particles occur within a short time θ of each other, the second one is not detected. Then, the probability density function of X, the time between successive recorded detections, is f(x) = k exp(−x/μ), for θ ≤ x < ∞.

Find k and the expectation of X.

Let Y be the smallest of a sample of n observations on X. Find the probability density function of Y .


I managed the first part and got k = (1/μ)exp(θ/μ) and E(X) = θ + μ

I'm not exactly sure how to begin the second part!? I know there are n x_i's in the range of X and that Y is the smallest of these x_i's, but then how do it write that? confused :s

Any help would be great.
 
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  • #2
For the second part, let Y be the smallest of the sample of n observations on X. The probability density function of Y is given by:fY (y) = n * fX (y) * FX (y)^(n−1) where FX (y) is the cumulative density function of X. Therefore, the probability density function of Y is fY (y) = n * k * exp(-y/μ) * (1 - exp(-y/μ))^(n−1) for θ ≤ y < ∞.
 

Related to Question involving pdf of change of variable

1. What is the PDF (probability density function) of a change of variable?

The PDF of a change of variable is a mathematical function that describes the probability of a random variable taking on a particular value after a transformation has been applied to it.

2. How is the PDF of a change of variable calculated?

The PDF of a change of variable is calculated using the Jacobian determinant, which is a measure of the change in volume between the original and transformed coordinate systems. It is multiplied by the PDF of the original variable to obtain the PDF of the transformed variable.

3. What is the significance of the PDF of a change of variable?

The PDF of a change of variable is important in many scientific fields, including statistics, physics, and engineering. It allows us to analyze and understand the behavior of transformed data, which is often necessary for solving complex problems.

4. Can the PDF of a change of variable be used for any type of transformation?

Yes, the PDF of a change of variable can be used for any type of transformation, as long as the transformation is one-to-one and differentiable. This means that each input value has a unique output value and the transformation can be described by a smooth curve.

5. How does the PDF of a change of variable affect the shape of a distribution?

The PDF of a change of variable can significantly alter the shape of a distribution. It can stretch, shrink, or skew the data, depending on the transformation used. This can have a big impact on statistical analyses and model building, so it is important to carefully consider the choice of transformation and its effect on the PDF.

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