# 13. Experiments and estimators#

Recommended reference: Wasserman [Was04], Sections 6.1 and 7.2 (in particular Examples 7.10 and 7.11).

In this section we give a very brief introduction to the concept of
*statistical inference*, in which we try to infer properties of an
unknown distribution from samples of the distribution.

The setting that we are thinking of is that of an experiment that gives us a collection of data points, or a process that we observe during a certain amount of time. We view our observations as random variables \(X_1,\ldots,X_n\). We want to make a “best guess” for the value of some unknown quantity behind our experiment. For example, suppose we have an object radiating heat and we take a picture with an infrared camera. Then we can try to estimate the temperature of the object from the values of the pixels in the image.

The concept of an *estimator* formalises how we estimate an unknown
quantity \(\theta\) from the observed values of \(X_1,\ldots,X_n\). An
estimator for \(\theta\), denoted by \(\hat\theta\), is just a function of
the observations \(X_1,\ldots,X_n\). Of course, we are interested in
estimators that tell us as much about the value of \(\theta\) as
possible.

Suppose \(X_1,\ldots,X_n\) are independent samples from an unknown
distribution. We already defined the *sample mean* in
Definition 12.1, but we will denote it in this context by
\(\hat\mu\). Thus

(Sample variance)

The *sample variance* of \(X_1,\ldots,X_n\) is

with \(\hat\mu\) as in (13.1).

Note

The sample variance can also be defined with a factor \(\frac{1}{n}\) instead of \(\frac{1}{n-1}\). The reason for the factor \(\frac{1}{n-1}\) is that we have already used the same data to estimate \(\mu\). If we knew the exact value \(\mu\) beforehand and used it instead of \(\hat\mu\) to define the sample variance, the factor \(\frac{1}{n}\) would be the correct one.

## 13.1. Exercises#

Suppose \(X_1, X_2, \ldots, X_6\) are independent samples from a Poisson distribution with unknown parameter \(\lambda\). Suppose we measure these and obtain the values 0, 1, 4, 3, 0, 3. Compute the sample mean \(\hat\mu\) and the sample variance \(\hat\sigma^2\) of the distribution. Can you guess the value of \(\lambda\)?