Order Statistics, Unbiasedness, and Expected Values

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In summary: Thanks for the help!In summary, the conversation discusses finding the unbiased estimator for \theta using a random sample from the uniform distribution. The estimator is given by \hat{\theta} = Y_{(n)} - \frac{n}{n+1} and to check for unbiasedness, E[\hat{\theta}] needs to equal \theta. The solution involves finding the density function g_{(n)}(y) for Y_{(n)} and solving the integral using partial integration.
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Providence88
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Homework Statement



Let Y1, Y2, ..., Yn denote a random sample from the uniform distribution on the interval ([tex]\theta[/tex], [tex]\theta + 1[/tex]). Let [tex]\hat{\theta} = Y_{(n)} - \frac{n}{n+1}[/tex]

Show that [tex]\hat{\theta}[/tex] is an unbiased estimator for [tex]\theta[/tex]

Homework Equations



Well, to check for unbiasedness, E([tex]\hat{\theta}[/tex]) should = [tex]\theta[/tex].

The difficulty for me arises when calculating [tex]g_{(n)}(y)[/tex], needed to find E[[tex]\hat{\theta}[/tex]]. The interval ([tex]\theta[/tex], [tex]\theta + 1[/tex]) seems to make this integral very complicated:

[tex]E[\hat{\theta}][/tex] = [tex]\int^{\theta + 1}_{\theta} yg_{(n)}(y)[/tex]

The Attempt at a Solution



I attempted to find [tex]g_{(n)}(y)[/tex], which I thought to be [tex]ny^{n-1}[/tex], but according to our solutions manual, it's actually [tex]n(y-\theta)^{n-1}[/tex], which I have no idea how that is concluded. And even if that is the true value of [tex]g_{(n)}(y)[/tex], the integral is still looking very daunting.

Any help? Thanks!
 
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  • #2
I am not quite sure what [itex]g_n(y)[/itex] is so you will have to explain why you thought it would equal [itex]n y^{n-1}[/itex]. As for the integral it is not so hard. You can solve it by partial integration.

[tex]
\int_{\theta}^{\theta+1} ny(y-\theta)^{n-1}dy=y (y-\theta)^n ]_\theta^{\theta+1}-\int_{\theta}^{\theta+1} (y-\theta)^n dy
[/tex]
 
  • #3
Oh, [tex]g_{(n)}(y)[/tex] is the density function for [tex]Y_{(n)}[/tex]=max(Y1, Y2, ..., Yn)

[tex]g_{(n)}(y) = n[F(Y)]^{n-1}*f(y)[/tex], where F(Y) is the distribution function of Y and f(y) is the density function. Since the bounds are theta and theta plus one, I assumed that f(y), by definition, is 1/(theta + one - theta), which equals one. If f(y) = 1, then F(Y) = y + C. I'm starting to think that the plus C would be -(theta).
 

FAQ: Order Statistics, Unbiasedness, and Expected Values

What is the definition of order statistics?

Order statistics refer to a set of statistical values that are arranged in ascending or descending order. These values can include minimum and maximum values, median, quartiles, and percentiles.

How is unbiasedness defined in statistics?

In statistics, unbiasedness refers to a property of an estimator or statistic that has an expected value equal to the true population parameter it is estimating. This means that on average, the estimator will provide an accurate estimate of the parameter.

What is the expected value of a random variable?

The expected value of a random variable is the long-term average value of the variable over many repeated trials. It is calculated by multiplying each possible value of the variable by its corresponding probability and summing all of these products.

How is the expected value of order statistics calculated?

The expected value of order statistics can be calculated by using the formula: E(Xᵢ) = (n+1)/(k+1), where n is the number of observations and k is the position of the statistic in the ordered data set (i.e. k=1 for minimum value, k=n for maximum value).

What is the purpose of using order statistics in statistical analysis?

Order statistics are used in statistical analysis to gain a better understanding of the distribution and variability of a data set. They can also be used to make comparisons between different data sets and to identify extreme values or outliers.

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