# Sufficient statistics and Sufficient Estimators

An estimator $\hat{\theta}$ is sufficient if it make so much use of the information in the sample that no other estimator could extract from the sample, additional information about the population parameter being estimated.

The sample mean $\overline{X}$ utilizes all the values included in the sample so it is sufficient estimator of population mean $\mu$.

Sufficient estimators are often used to develop the estimator that have minimum variance among all unbiased estimators (MVUE).

If sufficient estimator exists, no other estimator from the sample can provide additional information about the population being estimated.

If there is a sufficient estimator, then there is no need to consider any of the non-sufficient estimator. Good estimator are function of sufficient statistics.

Let $X_1,X_2,\cdots,X_n$ be a random sample from a probability distribution with unknown parameter $\theta$, then this statistic (estimator) $U=g(X_1,X_,\cdots,X_n)$ observation gives $U=g(X_1,X_2,\cdots,X_n)$ does not depend upon population parameter $\Theta$.

## Sufficient Statistic Example

The sample mean $\overline{X}$ is a sufficient for the population mean $\mu$ of a normal distribution with known variance. Once the sample mean is known, no further information about the population mean $\mu$ can be obtained from the sample itself, while median is not sufficient for the mean; even if the median of the sample is known, knowing the sample itself would provide further information about the population mean $\mu$.

## Mathematical Definition of Sufficiency

Suppose that $X_1,X_2,\cdots,X_n \sim p(x;\theta)$. $T$ is sufficient for $\theta$ if the conditional distribution of $X_1,X_2,\cdots, X_n|T$ does not depend upon $\theta$. Thus
$p(x_1,x_2,\cdots,x_n|t;\theta)=p(x_1,x_2,\cdots,x_n|t)$
This means that we can replace $X_1,X_2,\cdots,X_n$ with $T(X_1,X_2,\cdots,X_n)$ without losing information.