Question on Probability & Uniform Distribution.

In summary, the distribution of the first order statistic (minimum) of a sample of size N taken from a continuous uniform distribution is F(a)^n, while the distribution of the highest order statistic (maximum) is 1 - (1 - Fx(x))n. To determine these distributions, one can use the probability mass function of N and the concept of independence.
  • #1
Sunil12
3
0
Suppose a sample of random size N is taken from the continuous uniform(0, θ)
distribution, and N has a discrete distribution with p.m.f.

P (N = n) = 1/(n! (e − 1) ) for n = 1, 2, 3, . . . .

Determine the distribution of the
i) first order statistic (the minimum) of X1 , X2, . . . , XN .
ii) highest order statistic (the maximum) of X1, X2, . . ., XN .

Please help me to solve this problem.
 
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  • #2
Sounds like homework. What have you tried? What do you know in general about the distribution of order statistics?
 
  • #3
Generally the distribution of Xi in a order statistic is Binomial. Isn't it ? because given a value 'a' Xi is either Xi < a or Xi>=a. It is like a success failure.
 
  • #4
To get you started, P[max<=x|N=n] = P[X1<=x,...,Xn<=x]=P[X1<=x]^n, then simply take the expectation wrt N.
 
  • #5
Sunil12 said:
Generally the distribution of Xi in a order statistic is Binomial. Isn't it ? because given a value 'a' Xi is either Xi < a or Xi>=a. It is like a success failure.

Not quite. If you know that

[tex]
P(X_n \le a)
[/tex]

([itex] X_n [/itex] is the largest order statistic) then you know that ALL the other values are less than or equal to a, so

[tex]
P(X_n \le a) = P(X1 \le a \text{ and } X2 \le a \text{ and } \cdots \text{ and } Xn \le a)
= \left(P(X \le a)\right)^n = F(a)^n
[/tex]

by independence. To work with the minimum start with

[tex]
P(X_{(1)} > a)
[/tex]

and think about what it means for the smallest item in the sample to be larger than some value.
 
  • #6
FX1(a) = 1 - P(X1 > a)

which will essentially be 1 - (1 - Fx(x))n

Right ?
 

Related to Question on Probability & Uniform Distribution.

1. What is probability?

Probability is a measure of the likelihood or chance of an event occurring. It is often expressed as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty.

2. What is a uniform distribution?

A uniform distribution is a probability distribution where all outcomes have equal or nearly equal probability of occurring. This means that each possible outcome has the same chance of being observed.

3. How is probability related to a uniform distribution?

Probability and uniform distribution are closely related. A uniform distribution is one way to represent the probability of multiple outcomes. In a uniform distribution, the probability of each outcome is the same, making it easy to calculate the probability of a specific event occurring.

4. What are some real-life examples of a uniform distribution?

Some examples of a uniform distribution in real life include tossing a fair coin, rolling a fair die, and selecting a card from a shuffled deck. In each of these examples, each outcome has the same probability of occurring.

5. How can probability and a uniform distribution be used in scientific research?

Probability and uniform distribution can be used in scientific research to make predictions and draw conclusions based on data. By understanding the probability of certain outcomes, scientists can make informed decisions and analyze the likelihood of specific events occurring.

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