What is the Joint Density of C = min(A,B)?

In summary, the correct density of C=\min(A,B) is 2(\lambda+\mu)e^{-c(\lambda+\mu)}. While there were some errors in the initial computation, the method used to find the density was correct and led to the correct result. The mistake in the initial computation was in the calculation of f_C(c), where a factor of 2 was accidentally introduced. However, the final result was found correctly using the method of finding the probability that both A and B are greater than c.
  • #1
spitz
60
0

Homework Statement



I have:

[tex]f_A=\lambda e^{-\lambda a}[/tex]
[tex]f_B=\mu e^{-\mu b}[/tex]

([itex]A[/itex] and [itex]B[/itex] are independent)

I need to find the density of [itex]C=\min(A,B)[/itex]

2. The attempt at a solution
[tex]f_C(c)=f_A(c)+f_B(c)-f_A(c)F_B(c)-F_B(c)f_A(c)[/tex]
[tex]=\lambda e^{-\lambda c}+\mu e^{-\mu c}-\lambda e^{-\lambda c}(1-e^{-\mu c})-(1-e^{-\lambda c})\mu e^{-\mu c}[/tex]
[tex]=\lambda e^{-\lambda c}e^{-\mu c}+\mu e^{-\lambda c}e^{-\mu c}[/tex]
[tex]=2(\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Correct or utterly wrong?
 
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  • #2
spitz said:

Homework Statement



I have:

[tex]f_A=\lambda e^{-\lambda a}[/tex]
[tex]f_B=\mu e^{-\mu b}[/tex]

([itex]A[/itex] and [itex]B[/itex] are independent)

I need to find the density of [itex]C=\min(A,B)[/itex]

2. The attempt at a solution
[tex]f_C(c)=f_A(c)+f_B(c)-f_A(c)F_B(c)-F_B(c)f_A(c)[/tex]
[tex]=\lambda e^{-\lambda c}+\mu e^{-\mu c}-\lambda e^{-\lambda c}(1-e^{-\mu c})-(1-e^{-\lambda c})\mu e^{-\mu c}[/tex]
[tex]=\lambda e^{-\lambda c}e^{-\mu c}+\mu e^{-\lambda c}e^{-\mu c}[/tex]
[tex]=2(\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Correct or utterly wrong?

Some blunders (probably just typos), but answer is right: your first line should have been
[tex]f_C(c)=f_A(c)+f_B(c)-f_A(c)F_B(c)-F_A(c)f_B(c),[/tex]
which is what your later lines computed. However, you are doing it the hard way: much easier is to say [tex] \Pr \{\min(A,B) > c \} = \Pr \{ A > c \mbox{ and } B > c \}
= \Pr \{A > c \} \cdot \Pr \{ B > c \}. [/tex]

RGV
 
  • #3
Ray Vickson said:
...much easier is to say [tex] \Pr \{\min(A,B) > c \} = \Pr \{ A > c \mbox{ and } B > c \}
= \Pr \{A > c \} \cdot \Pr \{ B > c \}. [/tex]

RGV

[tex]=(1-F_A(c)) \cdot (1-F_B(c))=(1-(1-e^{-\lambda c})) \cdot (1-(1-e^{-\mu c}))=e^{-c(\lambda+\mu)}[/tex]
[tex]\Rightarrow\frac{d}{dc}e^{-c(\lambda+\mu)}=-(\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Although my first answer should have been: [tex](\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Which is correct?
 
Last edited:
  • #4
spitz said:
[tex]=(1-F_A(c)) \cdot (1-F_B(c))=(1-(1-e^{-\lambda c})) \cdot (1-(1-e^{-\mu c}))=e^{-c(\lambda+\mu)}[/tex]
[tex]\Rightarrow\frac{d}{dc}e^{-c(\lambda+\mu)}=-(\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Although my first answer should have been: [tex](\lambda+\mu)e^{-c(\lambda+\mu)}[/tex]

Which is correct?

Your first *answer* was incorrect, but your method was correct up to the second-last line; in my original response, I messed the factor of 2, so should not have said it was correct. Basically, to get your last line you said a+b = 2(a+b), so you made a blunder.

The result [itex] \min(A,B) \leftrightarrow (\lambda+\mu) e^{-(\lambda + \mu)t} [/itex] is correct. It is one of the absolutely standard properties of the exponential. Since the second way of getting it is correct, step-by-step, it cannot fail to be correct; you just need more confidence when making true statements, but you also need to be careful when doing algebraic manipulations.

RGV
 

Related to What is the Joint Density of C = min(A,B)?

What is a joint density problem?

A joint density problem is a statistical problem that involves determining the probability distribution of two or more random variables. It is used to model the relationship between these variables and to understand how they affect each other.

What is the importance of solving joint density problems?

Solving joint density problems allows us to understand the relationship between different variables and make predictions about their behavior. This information is useful in various fields such as economics, finance, and engineering.

What methods are used to solve joint density problems?

The most common methods used to solve joint density problems include the use of probability distributions, such as the normal distribution, and the application of statistical tools such as regression analysis and correlation analysis.

What are some common applications of joint density problems?

Joint density problems are commonly used in fields such as finance to model the relationship between stock prices and interest rates, in engineering to predict the behavior of different components in a system, and in medicine to study the relationship between different health factors.

How can I improve my skills in solving joint density problems?

To improve your skills in solving joint density problems, it is important to have a strong understanding of probability and statistics. It is also helpful to practice solving different types of problems and to consult with experienced professionals in the field.

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