A Question about an unbiased estimator

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In summary, the random sample X1,...,Xn has independent samples from a N(0,θ) distribution. In order to find an unbiased estimator for \sqrt{\theta}, the range of integration should be split into x < 0 and x > 0, and the absolute value of X_i should be integrated over each range.
  • #1
Artusartos
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Homework Statement



The random sample [itex]X_1, ... , X_n[/itex] has a [itex]N(0, \theta)[/itex] distribution. So now I have to solve for c such that [itex]Y= c \sum^n_{i=1}[/itex] is an unbiased estimator for [itex]\sqrt{\theta}[/itex].

Homework Equations


The Attempt at a Solution



[itex]E(c \sum^n_{i=1} |X_i|) = c \sum^n_{i=1} E(|X_i|) = c \sum^n_{i=1} \int \frac{|X_i|}{\sqrt{2(\pi)(\theta)}}e^{-X_i/(2\theta)}[/itex]

So now I have to solve...

[itex] c \sum^n_{i=1} \int \frac{|X_i|}{\sqrt{2(\pi)(\theta)}} e^{-X_i/(2\theta)} = \sqrt(\theta)[/itex], right? But how can I integrate the absolute value of [itex]X_i[/itex]?

Thanks in advance
 
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  • #2
Split the range of integration into x < 0, x > 0.
The random sample X1,...,Xn has a N(0,θ) distribution.
Do you mean, X1,...,Xn are independent samples from a N(0,θ) distribution? If so, why the subscript on θi?
 
  • #3
haruspex said:
Split the range of integration into x < 0, x > 0.

Do you mean, X1,...,Xn are independent samples from a N(0,θ) distribution? If so, why the subscript on θi?

Oh sorry, it's supposed to be just [itex]\theta[/itex]
 

Related to A Question about an unbiased estimator

1. What is an unbiased estimator?

An unbiased estimator is a statistical tool used to estimate a population parameter without any systematic error or bias. This means that, on average, the estimator will produce an estimate that is equal to the true value of the parameter.

2. How is an unbiased estimator different from a biased estimator?

A biased estimator is a statistical tool that consistently produces estimates that are either consistently above or below the true value of the population parameter. This means that the estimate is not accurate, and there is a systematic error or bias in the estimation process. An unbiased estimator, on the other hand, produces estimates that are, on average, equal to the true value.

3. How do you determine if an estimator is unbiased?

An estimator is considered unbiased if the expected value of the estimator is equal to the true value of the population parameter. This can be determined by performing multiple simulations or by using mathematical proofs.

4. Can an estimator be unbiased for one population parameter and biased for another?

Yes, it is possible for an estimator to be unbiased for one population parameter and biased for another. This depends on the specific properties of the estimator and the distribution of the data.

5. Why is it important for an estimator to be unbiased?

An unbiased estimator is desirable because it produces estimates that are, on average, equal to the true value of the population parameter. This means that it is a more accurate and reliable tool for making inferences about the population. Biased estimators can lead to incorrect conclusions and can affect the validity of statistical analyses.

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