Calculating Expected Value for Independent Random Variables

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SUMMARY

The discussion focuses on calculating the expected value of the maximum of independent and identically distributed random variables, denoted as E(max(X1,...,Xn)). Participants confirm that the cumulative distribution function (CDF) of the maximum, Y=max(X1,...,Xn), can be expressed as P(Y≤y) = P(X1≤y, X2≤y,..., Xn≤y), leading to the relationship P(Y ≤ y) = F_Y^n(y). This approach allows for deriving the probability density function (pdf) and subsequently calculating the expected value using standard methods.

PREREQUISITES
  • Understanding of independent and identically distributed (i.i.d.) random variables
  • Familiarity with cumulative distribution functions (CDF) and probability density functions (pdf)
  • Knowledge of order statistics
  • Basic principles of expected value calculation
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  • Study order statistics in-depth to understand their application in probability theory
  • Learn about the derivation of cumulative distribution functions for maximum values
  • Explore the properties of independent random variables in probability
  • Investigate advanced techniques for calculating expected values in continuous distributions
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Statisticians, data scientists, and anyone involved in probability theory or statistical analysis who seeks to understand the behavior of maximum values in random variables.

jakey
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Hi guys, if X1,X2,...,Xn are independent and identically distributed random variables, how do you find E(max(X1,...,Xn))?

Do you need to do order statistics or anything of that sort here? I got my answer by letting Y=max(X1,...Xn) and I got the CDF and then pdf of Y. For the CDF of Y, I just noted that P(Y<=y) = P(X1<=y, X2<=y,..., Xn<=y). Is this right?
 
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jakey said:
Hi guys, if X1,X2,...,Xn are independent and identically distributed random variables, how do you find E(max(X1,...,Xn))?

Do you need to do order statistics or anything of that sort here? I got my answer by letting Y=max(X1,...Xn) and I got the CDF and then pdf of Y. For the CDF of Y, I just noted that P(Y<=y) = P(X1<=y, X2<=y,..., Xn<=y). Is this right?

Yes: write
<br /> P(Y \le y) = P(X_1 \le y, \dots, X_n \le y) = F_Y^n(y)<br />

This allows you to get the density (assuming the quantities are continuous) and then you find the expected value the usual way.
 
Thanks statdad! :D
 

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