Expected value and variance of max{Y_1,Y_2}

Click For Summary
SUMMARY

The discussion focuses on calculating the probability density function (p.d.f.), expected value, and variance of the random variable X defined as the maximum of two independent uniform random variables Y_1 and Y_2 over the interval [1,2]. The cumulative distribution function (c.d.f.) is derived as \( F_X(x) = \begin{cases} 0 & \text{if } x < 1 \\ (2-x)^2 & \text{if } 1 \leq x \leq 2 \\ 1 & \text{if } x > 2 \end{cases} \). The expected value is calculated to be \( E(X) = \frac{4}{3} \) and the variance is \( V(X) = \frac{1}{18} \). A sign error in the derivation of the c.d.f. was noted, indicating that the maximum of two variables cannot have an expectation value below that of its arguments.

PREREQUISITES
  • Understanding of probability theory, specifically concepts of random variables and distributions.
  • Familiarity with uniform distribution properties.
  • Knowledge of cumulative and probability density functions (c.d.f. and p.d.f.).
  • Basic calculus for evaluating integrals to find expected values and variances.
NEXT STEPS
  • Study the derivation of the cumulative distribution function for independent random variables.
  • Learn about the properties and applications of uniform distributions in probability.
  • Explore the concept of variance and its significance in statistics.
  • Investigate the use of LaTeX for mathematical expressions in academic writing.
USEFUL FOR

Students studying probability and statistics, mathematicians, data scientists, and anyone interested in understanding the behavior of maximum values of random variables.

HaLAA
Messages
85
Reaction score
0

Homework Statement


Let Y_1,Y_2 be independent random variable with uniform distribution on the interval [1,2]. Define X=max{Y_1,Y_2}. Find p.d.f., expected value and variance.

Homework Equations

The Attempt at a Solution


Since $X=\max\{Y_1,Y_2\}$, this tells $Y_1$ and $Y_2$ must at most $x$. Thus, $\p(\max\{Y_1,Y_2\}\leq x)=\p(\{Y_1\leq x,Y_2\leq x\})=(2-x)^2$ with $1\leq x\leq 2$. So, the $c.d.f.$ of $X$ is
$$\p(x)=
\begin{cases}
0&\text{if $x<1$}\\
(2-x)^2 & \text{if $1\leq x\leq 2$}\\
1& \text{if $x>2$}
\end{cases}$$ where $a,b\in[1,2]$.
Then, the $p.d.f.$ is
$$p(x)=
\begin{cases}
2(2-x)&\;\;\text{if $1\leq a,b\leq 2$}\\
0&\;\;\text{otherwise}
\end{cases}$$
The expected value of $X$ is
$$E(X)=\int_{1}^{2}(4x-2x^2)\,dx=\frac{4}{3}$$
We also have
$$E(X^2)=\int_{1}^{2}(4x^2-2x^3)\,dx=\frac{11}{6}$$
Therefore, the variance is
$$V(X)=E(X^2)-(E(X))^2=\frac{11}{6}-\left(\frac{4}{3}\right)^2=\frac{1}{18}$$
 
Physics news on Phys.org
There is a sign error in the derivation of the cdf.

This is also visible in the expectation value: The maximum of two variables cannot have an expectation value below the expectation value of its arguments (which is 3/2 > 4/3).

By the way, you can use inline LaTeX with double # instead of single $.
 
HaLAA said:

Homework Statement


Let Y_1,Y_2 be independent random variable with uniform distribution on the interval [1,2]. Define X=max{Y_1,Y_2}. Find p.d.f., expected value and variance.

Homework Equations

The Attempt at a Solution


Since $X=\max\{Y_1,Y_2\}$, this tells $Y_1$ and $Y_2$ must at most $x$. Thus, $\p(\max\{Y_1,Y_2\}\leq x)=\p(\{Y_1\leq x,Y_2\leq x\})=(2-x)^2$ with $1\leq x\leq 2$. So, the $c.d.f.$ of $X$ is
$$\p(x)=
\begin{cases}
0&\text{if $x<1$}\\
(2-x)^2 & \text{if $1\leq x\leq 2$}\\
1& \text{if $x>2$}
\end{cases}$$ where $a,b\in[1,2]$.
Then, the $p.d.f.$ is
$$p(x)=
\begin{cases}
2(2-x)&\;\;\text{if $1\leq a,b\leq 2$}\\
0&\;\;\text{otherwise}
\end{cases}$$
The expected value of $X$ is
$$E(X)=\int_{1}^{2}(4x-2x^2)\,dx=\frac{4}{3}$$
We also have
$$E(X^2)=\int_{1}^{2}(4x^2-2x^3)\,dx=\frac{11}{6}$$
Therefore, the variance is
$$V(X)=E(X^2)-(E(X))^2=\frac{11}{6}-\left(\frac{4}{3}\right)^2=\frac{1}{18}$$
Your ##p(x)## is incorrect, because ##\{ \max(Y_1,Y_2) \leq x \} = \{ Y_1 \leq x \; \& \; Y_2 \leq x \}##, so ##P(X \leq x ) = P(Y_1 \leq x) \cdot P(Y_2 \leq x)## for independent ##Y_1,Y_2##.
 
Last edited:
Ray Vickson said:
Your ##p(x)## is incorrect, because ##\{ \max(Y_1,Y_2) \leq x \} = \{ Y_1 \leq x \; \& \; Y_2 \leq x \}##, so ##P(X \leq x ) = P(Y_1 \leq x) \cdot P(Y_2 \leq x)## for independent ##Y_1,Y_2##.
The cdf is (x-1)^2?
 
HaLAA said:
The cdf is (x-1)^2?

Not for all x, no, but maybe for some x. Anyway, you tell me!
 
Ray Vickson said:
Not for all x, no, but maybe for some x. Anyway, you tell me!
For x from 1 to 2, (x-1)^2
For x below 1, it is 0
For x greater than 2, it is 1
 
HaLAA said:
For x from 1 to 2, (x-1)^2
For x below 1, it is 0
For x greater than 2, it is 1

Correct. You could write it in LaTeX using the "cases" command, exactly as you did in post #1, like this:
$$F_X(x) = \begin{cases} 0 &\text{if $x < 1$}\\
(x-1)^2 & \text{if $1 \leq x < 2$}\\
1 & \text{if $x \geq 2$}
\end{cases}
$$
 
Last edited:

Similar threads

  • · Replies 5 ·
Replies
5
Views
1K
  • · Replies 6 ·
Replies
6
Views
2K
Replies
2
Views
1K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 3 ·
Replies
3
Views
1K
Replies
15
Views
3K
Replies
7
Views
4K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 11 ·
Replies
11
Views
3K