Density Function for X-Y on [0,1]

In summary, in order to calculate the density function for X-Y if X and Y are independent and continuously distributed on [0,1], you must first define Z as X - Y and graph X - Y <= Z on a graph. Then, find the limits of integration and solve for the cumulative distribution function. Finally, take the derivative of the CDF with respect to Z to get the density function.
  • #1
flybyme
20
0
hi..

Homework Statement


what's the density function for [tex]X-Y[/tex] if [tex]X[/tex] and [tex]Y[/tex] are independent and continously distributed on [tex][0,1][/tex]?
 
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  • #2
hi,

you must show some work before getting help; what have you tried?
 
  • #3
The answer of course depends on how X and Y are distributed. Are you given two specific distributions or do you want to calculate the answer in all its generality? See the Jacobi method to get a way of calculating the pdf of X-Y.
 
  • #4
ok... here's my try...

[tex]X[/tex] has the uniform distribution [tex]f_X(x) = 1[/tex]. to get the distribution for [tex]Y[/tex]: [tex]F_{-Y}(y) = P(-Y \leq y) = P(Y \geq -y) = 1 - F_Y(-y) \Rightarrow f_Y(y) = f_Y(-y) = -1[/tex]

the formula for convulsion in this case is [tex]f_Z(z) = \int_\infty^\infty f_X(z-y)f_Y(y)dy[/tex].

combining this with [tex]f_Y(y)[/tex] leads to [tex]f_Z(z) = -\int_0^1 f_X(z-y)dy[/tex]

the integrand is zero if the condition 0 <= z-y <= 1 (z-1 <= y <= z) isn't fulfilled.

we get three cases:

1. if 0 <= z <= 1: [tex]f_Z(z) = -\int^z_0 dy = -z[/tex]
2. if 1 < z <= 2: [tex]f_Z(z) = -\int^1_{z-1} dy = z - 2[/tex]
3. if z < 0 or z > 2: [tex]f_Z(z) = 0[/tex]

it seems correct to me, but I'm not sure..
 
  • #5
here is my hint:

1. first define X - Y as Z

2. then graph X - Y <= Z on a graph

3. find the limits of integration

4. solve it and this gives the cumulative distribution function

5. take the derivative of the CDF with respect to Z
 

FAQ: Density Function for X-Y on [0,1]

What is the definition of density function for X-Y on [0,1]?

The density function for X-Y on [0,1] is a mathematical function that describes the probability distribution of the random variable X-Y. It assigns a probability density to each possible value of X-Y within the range of [0,1].

How is the density function for X-Y on [0,1] calculated?

The density function for X-Y on [0,1] is calculated by taking the derivative of the cumulative distribution function (CDF) for X-Y. The CDF is the probability that X-Y will take on a value less than or equal to a given value in the range [0,1]. By taking the derivative of the CDF, we can determine the rate of change of the probability density at a specific value of X-Y.

What is the purpose of the density function for X-Y on [0,1]?

The density function for X-Y on [0,1] is used in statistics to model and analyze the probability distribution of X-Y. It allows us to calculate the likelihood of certain values of X-Y occurring and to make predictions about future events related to X-Y.

How is the density function for X-Y on [0,1] used in real-world applications?

The density function for X-Y on [0,1] is used in a variety of real-world applications, such as finance, economics, and engineering. It is commonly used to model stock prices, interest rates, and other variables that are affected by random fluctuations. It is also used in quality control and process improvement to analyze and improve the performance of systems.

Can the density function for X-Y on [0,1] have values greater than 1?

No, the density function for X-Y on [0,1] cannot have values greater than 1. This is because the area under the curve of the density function must equal 1, as it represents the total probability of all possible values of X-Y within the range of [0,1]. If the density function had values greater than 1, it would imply that the total probability is greater than 100%, which is not possible.

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