Is the Distribution of Unbiased Estimates the Same as the Statistic?

In summary, the conversation discusses the distribution of estimates for unbiased estimators and whether it is possible to have multiple distributions for the same parameter. The example of using sample mean as an estimator for the mean is given, with the conclusion that it is possible to have multiple unbiased estimators for the same parameter. The concept of MVUE (minimum variance unbiased estimator) is also mentioned.
  • #1
yhp266
6
0
Hi all, I've been confused about this for a while. Since it wasn't mentioned in class or my textbook, it probably reflects a fundamental lack of understanding :(


With any unbiased estimator, why is the distribution of the estimates also the distribution of the statistic?


Eg, suppose we have 5 independent and identically distributed normal random variables with variance 1 and mean (unknown parameter).

We observe some numbers say { 4, 5, -2 ,7 , 12}.

and we use sample mean as the estimator for mean. The sample mean is clearly normally distributed.

But why is this also the distribution for mean
 
Physics news on Phys.org
  • #2
And is it possible to have 2 different unbiased estimators for the same parameter?

Wouldnt it not make sense to have multiple distributions of estimates for a particular parameter
 
Last edited:
  • #3
1/I don't understand your first question.
2/Yes, many unbiased estimators for one parameter is possible. Let x1,x2,...,xn be a sample form N(mu,sigma). Then sample mean or any xi is unbiased for mu but the have different distributions. Look up what is ment by MVUE.
 

FAQ: Is the Distribution of Unbiased Estimates the Same as the Statistic?

What is the distribution of a statistic?

The distribution of a statistic refers to the pattern of values that the statistic can take on. It shows the frequency of each value and can provide information about the variability and central tendency of the data.

Why is it important to understand the distribution of a statistic?

Understanding the distribution of a statistic is important because it can help us make inferences about the population from which the data was collected. It can also assist in determining the appropriateness of certain statistical tests and methods.

How is the distribution of a statistic different from the distribution of data?

The distribution of a statistic is a summary of the data, while the distribution of data shows the individual values and their frequencies. The distribution of a statistic is based on a sample of the data, while the distribution of data is based on the entire population.

What are some common types of distributions for statistics?

Some common types of distributions for statistics include the normal distribution, binomial distribution, and chi-square distribution. Each type of distribution has its own unique shape and characteristics.

How can the distribution of a statistic be visualized?

The distribution of a statistic can be visualized through a variety of methods, including histograms, box plots, and probability plots. These visualizations can help us understand the shape and characteristics of the distribution.

Back
Top