# Method of moments estimators

Recall:

• "moments" of a distribution are E[X], E[X^2], E[X^3], ..., population or "distribution" moments.
• u = E[X] is a probability weighted average of the values in a population.
• Xbar is the average of the values in the sample.

Method of moments estimators (MMEs) equate the population and sample moments and solve for the unknown parameters.

The kth population moment:

u sub k = E[X^k], k > 0

The kth sample moment:

M sub k = 1/n sum(i=1 to n) (X sub i)^k, k > 0

# Example

X1, X2, ..., Xn iid ~ exp(rate = lambda)

first population moment: E[X] = 1/lambda # if you have multiple params use
                                         # higher order moments

first sample moment: 1/n sum(i=1 to n) Xsubi = Xbar

equate:

1/lambdahat = Xbar
=> lambdahat = 1/Xbar

This MME is not unbiased, but you can calculate the difference between it's expected value and lambda and then update the equation to compensate for that difference:

E[lambdahat] = n/n-1 lambda # not unbiased

E[n-1/n * 1/Xbar] = lambda # unbiased

lambdahat <- n-1/sum(i=1 to n)Xsubi