Law of large numbers for inverse of sample mean

So if \overline{X}_n converges to \mu, then by the continuous mapping theorem, \frac 1 {\overline{X}_n} converges to \frac 1 \mu. In summary, as long as \overline{X}_n does not equal 0, then \frac 1 {\overline{X}_n} converges to \frac 1 \mu according to the continuous mapping theorem.
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Homework Statement


If [tex]\overline{X}_n[/tex] converges to [tex]\mu[/tex], does [tex]\frac{1}{\overline{X}_n}[/tex] converge to [tex]\frac{1}{\mu}[/tex]?

Homework Equations


http://mathworld.wolfram.com/WeakLawofLargeNumbers.html

The Attempt at a Solution


[tex]\frac{1}{\overline{X}_n} = \frac{n}{X_1 + \cdots + X_n}[/tex]

[tex]E\left(\frac{1}{\overline{X}_n}\right) = E\left(\frac{n}{X_1 + \cdots + X_n}\right)[/tex]

[tex]E\left(\frac{1}{\overline{X}_n}\right) = n E\left(\frac{1}{X_1 + \cdots + X_n}\right)[/tex]

The RHS does not equal [tex]1/{\mu}[/tex] unless [tex]E\left(\frac{1}{X_1 + \cdots + X_n}\right) = \frac{1}{n \mu}[/tex] but how can I show that?

I know [tex]E\left( \frac{1}{\overline{X}_n} \right) \neq \frac{1}{\mu}[/tex] but I'm not sure how to apply that here.
 
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  • #2
Note that as long as [tex] x \ne 0 [/tex] the function [tex] x \to \frac 1 x [/tex] is continuous.
 

1. What is the "Law of large numbers" for inverse of sample mean?

The Law of large numbers for inverse of sample mean states that as the sample size increases, the inverse of the sample mean will approach the inverse of the population mean. This means that the average of the reciprocals of a large sample will be close to the reciprocal of the true average of the population.

2. Why is the "Law of large numbers" important in statistical analysis?

The Law of large numbers is important in statistical analysis because it provides a theoretical foundation for the use of sample means as estimators of population means. It allows us to make accurate predictions about the behavior of sample means as the sample size increases, which is crucial in making statistical inferences.

3. How is the "Law of large numbers" applied in real-life scenarios?

The Law of large numbers is applied in various real-life scenarios, such as opinion polls, market research, and quality control. In these situations, a small sample of individuals or products is used to make inferences about a larger population. The Law of large numbers helps ensure that these inferences are accurate and reliable.

4. Can the "Law of large numbers" be applied to any type of data?

Yes, the Law of large numbers can be applied to any type of data as long as the data is randomly selected from a larger population and the sample size is sufficiently large. This means that the data must be representative of the population and not biased in any way.

5. Are there any limitations to the "Law of large numbers"?

While the Law of large numbers is a powerful tool in statistical analysis, it does have some limitations. It assumes that the sample is selected randomly and that there is no bias in the data. It also requires a large enough sample size to accurately reflect the population. In some cases, the Law of large numbers may not hold, leading to inaccurate predictions and inferences.

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