Probability Density Function with an exponential random variable

In summary, the probability density function for the random variable Y defined by Y = \log X, where X is an exponential random variable with parameter \lambda = 1, is given by p(x) = e^x e^{-e^x}. However, there is no answer provided in the back of the book for this problem.
  • #1
lizzyb
168
0
The question is: if X is an exponential random variable with parameter [tex]\lambda = 1[/tex], compute the probability density function of the random variable Y defined by [tex]Y = \log X[/tex].

I did [tex]F_Y(y) = P \{ Y \leq y \} = P \{\log X \leq y \} = P \{ X \leq e^y \} = \int_{0}^{e^y} \lambda e^{- \lambda x} dx = \int_{0}^{e^y} e^{- x} dx = -e^{-x} \Big |_0^{e^y} = -e^{-e^y} + 1 = 1 - e^{-e^y}[/tex]

so

[tex] \frac {d F_{|X|} (x) } {dx} = p(x) = e^x e^{-e^x}[/tex]

Unfortunately the answer isn't in the back of the book. Does that look okay?
 
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  • #2
lizzyb said:
The question is: if X is an exponential random variable with parameter [tex]\lambda = 1[/tex], compute the probability density function of the random variable Y defined by [tex]Y = \log X[/tex].

I did [tex]F_Y(y) = P \{ Y \leq y \} = P \{\log X \leq y \} = P \{ X \leq e^y \}[/tex][tex] = \int_{0}^{e^y} \lambda e^{- \lambda x} dx = \int_{0}^{e^y} e^{- x} dx[/tex]
This step is wrong. Obviously you can't just drop the [itex]\lambda[/itex] like that. I suspect what you did was a substitution but you kept the same variable. If you let [itex]u= \lambda x[/itex], then du= \lambda dx so the integrand becomes e^u du. But you need to change the limits of integration also. When x= 0, u= 0 but when x= ey, u= \lambda e^y. The correct integral is now
[tex]\int_0^{\lambda e^y} e^{-u}du= -e^{-u}\right|_{0}^{\lambda e^y}[/tex]

[tex] = -e^{-x} \Big |_0^{e^y} = -e^{-e^y} + 1 = 1 - e^{-e^y}[/tex]

so

[tex] \frac {d F_{|X|} (x) } {dx} = p(x) = e^x e^{-e^x}[/tex]

Unfortunately the answer isn't in the back of the book. Does that look okay?
 
  • #3
Since [tex]\lambda = 1[/tex] I just sort of dropped/substituted it. Thanks :-)
 

1. What is a Probability Density Function (PDF) with an exponential random variable?

A Probability Density Function (PDF) with an exponential random variable is a mathematical function that describes the probability of a continuous random variable taking on a certain value. In the case of an exponential random variable, the PDF is calculated using the parameter lambda (λ) and is represented by the formula f(x) = λe^(-λx), where x ≥ 0.

2. How is a PDF with an exponential random variable different from other probability distributions?

A PDF with an exponential random variable is different from other probability distributions in several ways. One key difference is that it is a continuous distribution, meaning that the random variable can take on any value within a certain range. Additionally, the exponential distribution is often used to model waiting times, as it represents the probability of waiting a certain amount of time for an event to occur.

3. What is the relationship between the PDF and the Cumulative Distribution Function (CDF) for an exponential random variable?

The Cumulative Distribution Function (CDF) for an exponential random variable is the integral of the PDF, and represents the probability that the random variable will take on a value less than or equal to a given value. In other words, the CDF is the accumulation of the PDF over a range of values.

4. How is the parameter lambda (λ) related to the shape of the PDF for an exponential random variable?

The parameter lambda (λ) is a measure of the scale or rate of the exponential distribution. In other words, it determines the shape of the PDF, with larger values of lambda resulting in a steeper slope and a more sharply peaked distribution. A smaller value of lambda will result in a flatter, more spread out distribution.

5. What are some common applications of the exponential distribution and its PDF?

The exponential distribution and its PDF have many practical applications in fields such as engineering, finance, and biology. Some common applications include modeling the time between arrivals in a queue or the time between failures of a machine, predicting the lifespan of electronic components, and analyzing the time between mutations in genetic material.

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