CDF of minimum of N random variables.

For example, always use ##\geq## and ##\leq## in all of your inequalities, and never use ##>## or ##<##. Also, always use the words "at least" or "at most" in your explanations, never the word "only" or the word "exactly".
  • #1
ashwinnarayan
18
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There's this problem that I've been trying to solve. I know the solution for it now but my initial attempt at a solution was wrong and I can't seem to figure out the mistake with my reasoning. I'd appreciate some help with figuring this one out.

1. Homework Statement
I have a set of random variables drawn independently from a distribution. And a new random variable.

[tex]Z = min\{X_1, X_2, ... X_N\}[/tex].

Each [itex]X_i[/itex] has the pdf [itex]f_X(x)[/itex] and CDF [itex]F_X(x)[/itex]

What I want to do is to find the CDF (and then the PDF) of Z.

The Attempt at a Solution


So here's what I tried first.

[tex]P(Z<z) = P((\exists i\ s.t\ X_i < z) \cap (X_j > z\ \forall j \neq i)) [/tex]
[tex] P(Z<z) = \left(\sum_{i=1}^{N}P(X_i < z)\right) \left( \sum_{j=1, j\neq i}^{N}P(X_j < z) \right)[/tex]
[tex] P(Z<z) = N(N-1)F_X(z)(1-F_X(z))[/tex]

But I know this is wrong because I did some research and I know that the correct (and easier) way to do it is to find [itex]P(Z > z)[/itex]. The actual answer is [itex]1 - (1 - F_X(z))^N [/itex].

Can someone help me find the flaw in my reasoning?
 
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  • #2
ashwinnarayan said:
There's this problem that I've been trying to solve. I know the solution for it now but my initial attempt at a solution was wrong and I can't seem to figure out the mistake with my reasoning. I'd appreciate some help with figuring this one out.

1. Homework Statement
I have a set of random variables drawn independently from a distribution. And a new random variable.

[tex]Z = min\{X_1, X_2, ... X_N\}[/tex].

Each [itex]X_i[/itex] has the pdf [itex]f_X(x)[/itex] and CDF [itex]F_X(x)[/itex]

What I want to do is to find the CDF (and then the PDF) of Z.

The Attempt at a Solution


So here's what I tried first.

[tex]P(Z<z) = P((\exists i\ s.t\ X_i < z) \cap (X_j > z\ \forall j \neq i)) [/tex]
[tex] P(Z<z) = \left(\sum_{i=1}^{N}P(X_i < z)\right) \left( \sum_{j=1, j\neq i}^{N}P(X_j < z) \right)[/tex]
[tex] P(Z<z) = N(N-1)F_X(z)(1-F_X(z))[/tex]

But I know this is wrong because I did some research and I know that the correct (and easier) way to do it is to find [itex]P(Z > z)[/itex]. The actual answer is [itex]1 - (1 - F_X(z))^N [/itex].

Can someone help me find the flaw in my reasoning?

You are claiming that ##Z < z## if and only if exactly one of the ##X_i## is ##< z## while all of the others are ##> z##. This claim is false: ##\min\{3,4,5 \} < 10## but none of 3,4 or 5 is > 10. Also, ##\min \{3,4,5 \} < 4.5 ## but only one of the entries exceeds 4.5.

Also: be careful of inequalities. The usual definition of CDF is ##P(Z \leq z)##, with a non-strict inequality. Some authors (very few) write the CDF as ##P(Z < z)##, but in that case the complementary probability is NOT ##P(Z > z)##, but rather, ##P(Z \geq z)##. Of course, it makes no difference when you are dealing with continuous random variables having densities (as you seem to be), but if you want to deal with discrete, or mixed continuous-discrete random variables, then you must be very careful. The easiest way to be careful is to learn some rigid rules right from the start of your studies.
 
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What is the CDF of the minimum of N random variables?

The CDF (cumulative distribution function) of the minimum of N random variables is a function that describes the probability that the minimum value of a set of N random variables will be less than or equal to a given value. It is often used in statistical analysis to calculate the probability of extreme events or to model the behavior of complex systems.

How is the CDF of the minimum of N random variables calculated?

The CDF of the minimum of N random variables is calculated by taking the minimum of the individual CDFs for each random variable. This can be done using mathematical formulas or by using statistical software.

What is the relationship between the CDF of the minimum of N random variables and the CDF of the individual random variables?

The CDF of the minimum of N random variables is always less than or equal to the CDF of the individual random variables. This is because the minimum value of a set of numbers can never be greater than any of the individual numbers in the set.

What are some practical applications of the CDF of the minimum of N random variables?

The CDF of the minimum of N random variables is commonly used in risk analysis, reliability engineering, and extreme value theory. It can also be used in finance to model the behavior of stock prices or in weather forecasting to predict the occurrence of extreme weather events.

Can the CDF of the minimum of N random variables be used to determine the probability of a specific event occurring?

Yes, the CDF of the minimum of N random variables can be used to calculate the probability of a specific event occurring. This is done by finding the value on the x-axis that corresponds to the desired probability on the y-axis. This value represents the threshold for the minimum value of N random variables, and the probability of the event occurring is equal to the CDF at this threshold.

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