CDF, and setting the integral for this

In summary, the conversation is about finding the bounds and solving a double integral for a probability density function. The conversation also discusses splitting the integral into two parts and integrating with respect to x or y first. The correct bounds for the integral are -x<y<x and 0<x<infinity, with c=1/4. Part d of the problem involves finding the expected value of Y given X=x, which is solved by computing the marginal distribution of X and the conditional distribution function of Y. The final answer is 0. Overall, the conversation provides guidance on how to solve the given problem.
  • #1
nacho-man
171
0
Please refer to the attached image.

I can't quite get the bounds for question a) right. it's so confusing. Would it be wise to split the double integral into two parts? I guess that's usually favourable with when dealing with absolute values. But the bounds are still confusing me.

would it be easier to integrate with respect to x first, or w.r.t y?For x, it would just be 0 to infinity.

for y it would be -infinity to x ? whilst splitting the integral with respect to x? is this correct.
Actually i'll have a crack at it now. some as i make these question threads i tend to find a solution/or at least get closer to it.

Still, help/correction is very much appreciated.
Thanks!

edit: first attempt i did was
$c \int \int xe^{-x}dxdy = 1$ from $0 \ to \ \infty$ and $-\infty \ to \ x$. this was wrong, as it left me with a variable in my answer, so i will try again but int w.r.t y first.

edit 2: This also did not workedit 3: SOLVED PART A)
i used the bounds -x<y<x and 0<x<infinity
and obtained c= 1/2

edit 4: SOLVED PART B)
I got fy|x(x,y) = $\frac{1}{4x^{3}}$
and fx|y(x,y) = $xe^{-x}$

edit 5: I'm having trouble with part d

E[Y|X=e] = $\int y f_{x|y}(y|x)dy$
 

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  • #2
nacho said:
Please refer to the attached image.

I can't quite get the bounds for question a) right. it's so confusing. Would it be wise to split the double integral into two parts? I guess that's usually favourable with when dealing with absolute values. But the bounds are still confusing me.

would it be easier to integrate with respect to x first, or w.r.t y?For x, it would just be 0 to infinity.

for y it would be -infinity to x ? whilst splitting the integral with respect to x? is this correct.
Actually i'll have a crack at it now. some as i make these question threads i tend to find a solution/or at least get closer to it.

Still, help/correction is very much appreciated.
Thanks!

edit: first attempt i did was
$c \int \int xe^{-x}dxdy = 1$ from $0 \ to \ \infty$ and $-\infty \ to \ x$. this was wrong, as it left me with a variable in my answer, so i will try again but int w.r.t y first.

edit 2: This also did not workedit 3: SOLVED PART A)
i used the bounds -x<y<x and 0<x<infinity
and obtained c= 1/2

edit 4: SOLVED PART B)
I got fy|x(x,y) = $\frac{1}{4x^{3}}$
and fx|y(x,y) = $xe^{-x}$

What is not full clear is that the p.d.f. is defined as...

$\displaystyle f(x,y)= c\ x\ e^{- x}\ (1)$

... for $\displaystyle x>0,\ |y|< x$ and is 0 in the rest of the x,y plane. In that case the condition for c is...

$\displaystyle c\ \int_{0}^{\infty} \int_{-x}^{+ x} x\ e^{-x}\ d x\ d y = c\ \int_{0}^{\infty} 2\ x^{2}\ e^{- x }\ dx = 4\ c = 1 \implies c= \frac{1}{4}\ (1)$

The remaining part can be attacked with the procedure we already saw...

Kind regards

$\chi$ $\sigma$
 
  • #3
chisigma said:
What is not full clear is that the p.d.f. is defined as...

$\displaystyle f(x,y)= c\ x\ e^{- x}\ (1)$

... for $\displaystyle x>0,\ |y|< x$ and is 0 in the rest of the x,y plane. In that case the condition for c is...

$\displaystyle c\ \int_{0}^{\infty} \int_{-x}^{+ x} x\ e^{-x}\ d x\ d y = c\ \int_{0}^{\infty} 2\ x^{2}\ e^{- x }\ dx = 4\ c = 1 \implies c= \frac{1}{4}\ (1)$

The remaining part can be attacked with the procedure we already saw...

Kind regards

$\chi$ $\sigma$

thanks, i had just forgotten to multiply by 2 during a step, leading to me getting 1/2.

can you assist witg part d?i keep getting 0, which i think is wrong
 
  • #4
nacho said:
thanks, i had just forgotten to multiply by 2 during a step, leading to me getting 1/2.

can you assist witg part d?i keep getting 0, which i think is wrong

Answering to point d) is performed in three steps...

...first we conpute the marginal distribution of X...

$\displaystyle f_{x} (x) = \frac{1}{4}\ \int_{- x}^{+ x} x\ e^{- x}\ dx = \frac{x^{2}}{2}\ e^{-x}\ (1)$

... second the conditional distribution function of Y...

$\displaystyle f_{y} (y | X=x) = \frac{f(x,y)}{f_{x} (x)} = \frac{1} {2\ x}\ (2)$

... and third...

$\displaystyle E \{Y | X=x \} = \int_{- x}^{ + x} \frac{y}{2\ x}\ d y =0\ (3)$

Kind regards

$\chi$ $\sigma$
 
  • #5
chisigma said:
Answering to point d) is performed in three steps...

...first we conpute the marginal distribution of X...

$\displaystyle f_{x} (x) = \frac{1}{4}\ \int_{- x}^{+ x} x\ e^{- x}\ dx = \frac{x^{2}}{2}\ e^{-x}\ (1)$

... second the conditional distribution function of Y...

$\displaystyle f_{y} (y | X=x) = \frac{f(x,y)}{f_{x} (x)} = \frac{1} {2\ x}\ (2)$

... and third...

$\displaystyle E \{Y | X=x \} = \int_{- x}^{ + x} \frac{y}{2\ x}\ d y =0\ (3)$

Kind regards

$\chi$ $\sigma$

ah, i was right then, gues i should be more confident
 

FAQ: CDF, and setting the integral for this

What is CDF?

CDF stands for Cumulative Distribution Function. It is a mathematical function that describes the probability of a random variable taking on a value less than or equal to a specific value.

How is CDF related to PDF?

The CDF can be calculated from the Probability Density Function (PDF) by taking the integral of the PDF from negative infinity to the desired value. Conversely, the PDF can be calculated from the CDF by taking the derivative of the CDF.

What is the importance of setting the integral for CDF?

Setting the integral for the CDF is important because it allows us to determine the probability of a random variable taking on a range of values. By setting the integral, we can determine the likelihood of obtaining a value within a certain range, which is useful in statistical analysis and decision-making.

What is the domain of a CDF?

The domain of a CDF is the set of all possible values that a random variable can take on. It is a continuous range from negative infinity to positive infinity.

How is the CDF used in hypothesis testing?

In hypothesis testing, the CDF is used to calculate the p-value, which represents the probability of obtaining a result at least as extreme as the observed result. This allows us to determine the significance of our test and make conclusions about the population from which the sample was taken.

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