Expected value of a third order statistic?

In summary, the problem involves finding the expected value of the third highest value out of four i.i.d. random variables, X1, X2, X3, X4, which are uniformly distributed on [0,1]. The formula for E(Y1) (highest) and E(Y2) (second highest) are known, but the solution for E(Y3) (third highest) is needed. After trying different approaches, it was discovered that the formula for E(Y3) is similar to that of E(Y2), with a slight difference in the integration limits. The final answer is E(Y3) = 2/5.
  • #1
JamesF
14
0
Hi all. I'm struggling with this HW question. I've searched through the textbook and on the web and have been unable to find a solution

Homework Statement


I've got 4 i.i.d. random variables, X1, X2, X3, X4. Uniformly distributed on [0,1]
so the pdf = 1
and cdf F(x_i) = x_i

Let Y3 = the third highest value of the 4 variables.

What is E(Y3)? I know the answer is 2/5. But I can't figure out how to show this.

Homework Equations



I know the formula for E(Y2) (second highest) and E(Y1) (highest), but I cannot find anything about the expectation of the third highest.

[tex] E(Y_1) = \int_{0}^{1} y \cdot 4y^3 \, dy = 4/5 [/tex]
[tex] E(Y_2) = \int_{0}^{1} y \cdot (12y^2 - 12y^3) \, dy = 3/5 [/tex]

based on the pattern, one would assume that E(Y3) = 2/5 (plus I know that's the answer), but I can't derive the formula.

The Attempt at a Solution


I've tried a bunch of different approaches, but they've all been dead ends. Maybe I'm misunderstanding the problem. Or maybe just misunderstanding the concept of order statistics entirely.
 
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  • #2
alright I figured out my problem. I had it backwards. Our teacher uses (Y1) to denote the highest value of the 4 random variables, but the stuff I found on the web used Y1 to denote the LOWEST value.

so [tex] f_{Y_3} = \frac{4!}{1! \cdot 2!} y \cdot (1-y)^2 \cdot 1 [/tex]

integrate to find the expected value, and we get

[tex] E(Y_3) = \int_{0}^{1} y \cdot (12y(y-1)^2) dy = \frac{2}{5} [/tex]

tahdah!
 

1. What is the expected value of a third order statistic?

The expected value of a third order statistic is the average value that would be obtained if the third largest value in a sample were repeatedly selected. It is also known as the third order moment or the third smallest moment.

2. How is the expected value of a third order statistic calculated?

The expected value of a third order statistic can be calculated by taking the sum of all possible values of the third order statistic, each multiplied by its probability of occurring. This is also known as the weighted average or mean.

3. Why is the expected value of a third order statistic important?

The expected value of a third order statistic is important because it provides a measure of central tendency for a set of data. It can help in making predictions and understanding the behavior of a random variable or a sample.

4. How does the expected value of a third order statistic differ from the mean?

The expected value of a third order statistic differs from the mean in that it takes into account the order of values in a sample, while the mean only considers the numerical values. Additionally, the expected value of a third order statistic can be influenced by outliers or extreme values in the data.

5. Can the expected value of a third order statistic be negative?

Yes, the expected value of a third order statistic can be negative. This can occur if the data set has a large number of negative values or if the data is skewed towards lower values. However, in most cases, the expected value is a positive number.

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