Order Statistics Probabilities

In summary: FX(1)(x) = P(X(1) <= x)= P(X1,...,Xn <= X)=1-P(X1,...,Xn > X)=1-P(X1>X)^n since the xi's are iid.=1-[1-P(X1 <= X)]^n=1-[1-F(x)]^n
  • #1
mathmajor23
29
0

Homework Statement


Let Xi ~ iid f(x) = (2x)I[0,1](x), i = 1,...,n.

Find the distribution of X(1). What is the probability that the smallest one exceeds .2?
 
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  • #2
mathmajor23 said:

Homework Statement


Let Xi ~ iid f(x) = (2x)I[0,1](x), i = 1,...,n.

Find the distribution of X(1). What is the probability that the smallest one exceeds .2?

What work have you done on this so far? You need to show an attempt to solve this yourself before we can help.

RGV
 
  • #3
FX(1)(x) = P(X(1) <= x)
= P(X1,...,Xn <= X)
=1-P(X1,...,Xn > X)
=1-P(X1>X)^n since the xi's are iid.
=1-[1-P(X1 <= X)]^n
=1-[1-F(x)]^n
=1-[1-∫ from 0 to x (2tdt)]^n
=1-(1-X^2)^n

For the probability, P(X1>.2) = 1-Fx(1) (0.2) = (1-(0.2)^2)^n = (0.96)^n
 
  • #4
mathmajor23 said:
FX(1)(x) = P(X(1) <= x)
= P(X1,...,Xn <= X)
=1-P(X1,...,Xn > X)
=1-P(X1>X)^n since the xi's are iid.
=1-[1-P(X1 <= X)]^n
=1-[1-F(x)]^n
=1-[1-∫ from 0 to x (2tdt)]^n
=1-(1-X^2)^n

For the probability, P(X1>.2) = 1-Fx(1) (0.2) = (1-(0.2)^2)^n = (0.96)^n

I hope you were just being sloppy and don't really believe that
[tex] P(X_1, X_2, \ldots, X_n \leq x) = 1 - P(X_1, X_2, \ldots, X_n > x) \, \leftarrow \text{FALSE!}[/tex] because that is not valid. For two events A and B we have P(A) = 1 - P(B) if the events A and B are complementary (that is, have no points in common and together cover the entire sample sample space). Do the events [itex] \{X_1, \ldots,X_n \leq x \}[/itex] and [itex] \{X_1,\dots,X_n > x\}[/itex] look complementary to you? (Try drawing these for n = 2.) Of course, what is true is that [itex] P(X_i > x) = 1-P(X_i \leq x)[/itex] for each [itex] X_i[/itex] separately. You need to decide whether or not we have
[tex] P(X_1,\ldots,X_n > x) = (1-x^2)^n \text{ or } P(X_1,\ldots,X_n > x) = 1-(1-x^2)^n \,.[/tex]

RGV
 
Last edited:

1. What is the definition of order statistics probabilities?

Order statistics probabilities refer to the probabilities associated with the arrangement of a set of numbers in ascending or descending order. It is commonly used in statistics to analyze the distribution of a set of data.

2. How do you calculate the order statistics probabilities?

The order statistics probabilities can be calculated using the formula P(X≤x) = F(x)^n, where P(X≤x) is the probability of x or less numbers, F(x) is the cumulative distribution function, and n is the number of terms in the set.

3. What is the importance of order statistics probabilities in data analysis?

Order statistics probabilities help in determining the relative position of a data point within a set of data and provide insights into the distribution of the data. It is also used in hypothesis testing and estimating various statistical parameters.

4. Can order statistics probabilities be used for non-numerical data?

No, order statistics probabilities can only be used for numerical data. It is not applicable to non-numerical data such as categories or labels.

5. How can order statistics probabilities be applied in real-life situations?

Order statistics probabilities can be applied in various fields such as finance, economics, and engineering to analyze and interpret data. It can also be used for decision making, forecasting, and risk analysis.

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