Estimates and Estimation

Unbiasedness is a statistical concept that describes the accuracy of an estimator. An estimator is said to be an unbiased estimator if its expected value (or average value over many samples) equals the corresponding population parameter, that is, $E(\hat{\theta}) = \theta$.

If the expected value of an estimator $\theta$ is not equal to the corresponding parameter then the estimator will be biased. The bias of an estimator of $\hat{\theta}$ can be defined as

$$Bias = E(\hat{\theta}) – \theta$$

Note that $\overline{X}$ is an unbiased estimator of the mean of a population. Therefore,

• $\overline{X}$ is an unbiased estimator of the parameter $\mu$ in Normal distribution.
• $\overline{X}$ is an unbiased estimator of the parameter $p$ in the Bernoulli distribution.
• $\overline{X}$ is an unbiased estimator of the parameter $\lambda$ in the Poisson distribution.

However, the expected value of the sample variance $S^2=\frac{\sum\limits_{i=1}^n (X_i – \overline{X})^2 }{n}$ is not equal to the population variance, that is $E(S^2) = \sigma^2$.

Therefore, sample variance is not an unbiased estimator of the population variance $\sigma^2$.

Note that it is possible to have more than one unbiased estimator for an unknown parameter. For example, the sample mean and sample median are both unbiased estimators of the population mean $\mu$ if the population distribution is symmetrical.

Question: Show that the sample mean is an unbiased estimator of the population mean.

Solution:

Let $X_1, X_2, \cdots, X_n$ be a random sample of size $n$ from a population having mean $\mu$. The sample mean is $\overline{X}$ is

$$\overline{X} = \frac{1}{n} \sum\limits_{i=1}^n X_i$$

We must show that $E(\overline{X})=\mu$, therefore, taking the expectation on both sides,

\begin{align*}
E(\overline{X}) &= E\left[\frac{1}{n} \Sigma X_i \right]\\
&= \frac{1}{n} E(X_i) = \frac{1}{n} E(X_1 + X_2 + \cdots + X_n)\\
&= \frac{1}{n} \left[E(X_1) + E(X_2) + \cdots + E(X_n) \right]
\end{align*}

Since, in the random sample, the random variables $X_1, X_2, \cdots, X_n$ are all independent and each has the same distribution of the population, then $E(X_1)=E(X_2)=\cdots=E(X_n)$. So,

$$E(\overline{x}) = \frac{1}{n}(\mu+\mu+\cdots + \mu) = \mu$$

Why Unbiasedness is Important

• Accuracy: Unbiasedness is a measure of accuracy, not precision. Unbiased estimators provide accurate estimates on average, reducing the risk of systematic errors. However, an unbiased estimator can still have a large variance, meaning its individual estimates can be far from the true value.
• Consistency: An unbiased estimator is not necessarily consistent. Consistency refers to the tendency of an estimator to converge to the true value as the sample size increases.
• Foundation for Further Analysis: Unbiased estimators are often used as building blocks for more complex statistical procedures.

Unbiasedness Example

Imagine you’re trying to estimate the average height of students in your university. If you randomly sample 100 students and calculate their average height, this average is an estimator of the true average height of all students in that university. If this average height is consistently equal to the true average height of the entire student population, then your estimator is unbiased.

Unbiasedness is the state of being free from bias, prejudice, or favoritism. It can also mean being able to judge fairly without being influenced by one’s own opinions. In statistics, it also refers to (i) A sample that is not affected by extraneous factors or selectivity (ii) An estimator that has an expected value that is equal to the parameter being estimated.

Applications and Uses of Unbiasedness

• Parameter Estimation:
  • Mean: The sample mean is an unbiased estimator of the population mean.
  • Variance: The sample variance, with a slight adjustment (Bessel’s correction), is an unbiased estimator of the population variance.
  • Regression Coefficients: In linear regression, the ordinary least squares (OLS) estimators of the regression coefficients are unbiased under certain assumptions.
• Hypothesis Testing:
  • Unbiased estimators are often used in hypothesis tests to make inferences about population parameters. For example, the t-test for comparing means relies on the assumption that the sample means are unbiased estimators of the population means.
• Machine Learning: In some machine learning algorithms, unbiased estimators are preferred for model parameters to avoid systematic errors.
• Survey Sampling: Unbiased sampling techniques, such as simple random sampling, are used to ensure that the sample is representative of the population and that the estimates obtained from the sample are unbiased.

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Introduction (Properties of a Good Estimator)

The post is about a comprehensive discussion of the Properties of a Good Estimator. In statistics, an estimator is a function of sample data used to estimate an unknown population parameter. A good estimator is both efficient and unbiased. An estimator is considered as a good estimator if it satisfies the following properties:

• Unbiasedness
• Consistency
• Efficiency
• Sufficiency
• Invariance

Let us discuss these properties of a good estimator one by one.

Unbiasedness

An estimator is said to be an unbiased estimator if its expected value (that is mean of its sampling distribution) is equal to its true population parameter value. Let $\hat{\theta}$ be an unbiased estimator of its true population parameter $\theta$ then $\hat{\theta}$. If $E(\hat{\theta}) = E(\theta)$ the estimator ($\hat{\theta}$) will be unbiased. If $E(\hat{\theta})\ne \theta$, then $\hat{\theta}$ will be a biased estimator of $\theta$.

• If $E(\hat{\theta}) > \theta$, then $\hat{\theta}$ will be positively biased.
• If $E(\hat{\theta}) < \theta$, then $\hat{\theta}$ will be negatively biased.

Some examples of biased or unbiased estimators are:

• $\overline{X}$ is an unbiased estimator of $\mu$, that is, $E(\overline{X}) = \mu$
• $\widetilde{X}$ is also an unbiased estimator when the population is normally distributed, that is, $E(\widetilde{X}) =\mu$
• Sample variance $S^2$ is biased estimator of $\sigma^2$, that is, $E(S^2)\ne \sigma^2$
• $\hat{p} = \frac{x}{n}$ is an unbiased estimator of $E(\hat{p})=p$

It means that if the sampling process is repeated many times and calculations about the estimator for each sample are made, the average of these estimates would be very close to the true population parameter.

An unbiased estimator does not systematically overestimate or underestimate the true parameter.

Consistency

An estimator is said to be a consistent estimator if the statistic to be used as an estimator approaches the true population parameter value by increasing the sample size. OR
An estimator $\hat{\theta}$ is called a consistent estimator of $\theta$ if the probability that $\hat{\theta}$ becomes closer and closer to $\theta$, approaches unity with increasing the sample size.

Symbolically, $\hat{\theta}$ is a consistent estimator of the parameter $\theta$ if for any arbitrary small positive quantity $e$ or $\epsilon$.

\begin{align*}
\lim\limits_{n\rightarrow \infty} P\left[|\hat{\theta}-\theta|\le \varepsilon\right] &= 1\\
\lim\limits_{n\rightarrow \infty} P\left[|\hat{\theta}-\theta|> \varepsilon\right] &= 0
\end{align*}

A consistent estimator may or may not be unbiased. The sample mean $\overline{X}=\frac{\Sigma X_i}{n}$ and sample proportion $\hat{p} = \frac{x}{n}$ are unbiased estimators of $\mu$ and $p$, respectively and are also consistent.

It means that as one collects more and more data, the estimator becomes more and more accurate in approximating the true population value.

An efficient estimator is less likely to produce extreme values, making it more reliable.

Efficiency

An unbiased estimator is said to be efficient if the variance of its sampling distribution is smaller than that of the sampling distribution of any other unbiased estimator of the same parameter. Suppose there are two unbiased estimators $T_1$ and $T_2$ of the sample parameter $\theta$, then $T_1$ will be said to be a more efficient estimator compared to the $T_2$ if $Var(T_1) < Var(T_2)$. The relative efficiency of $T_1$ compared to $T_2$ is given by the ration

$$E = \frac{Var(T_2)}{Var(T_1)} > 1$$

Note that when two estimators are biased then MSE is used to compare.

A more efficient estimator has a smaller sampling error, meaning it is less likely to deviate significantly from the true population parameter.

An efficient estimator is less likely to produce extreme values, making it more reliable.

Sufficiency

An estimator is said to be sufficient if the statistic used as an estimator utilizes all the information contained in the sample. Any statistic that is not computed from all values in the sample is not a sufficient estimator. The sample mean $\overline{X}=\frac{\Sigma X}{n}$ and sample proportion $\hat{p} = \frac{x}{n}$ are sufficient estimators of the population mean $\mu$ and population proportion $p$, respectively but the median is not a sufficient estimator because it does not use all the information contained in the sample.

A sufficient estimator provides us with maximum information as it is close to a population which is why, it also measures variability.

A sufficient estimator captures all the useful information from the data without any loss.

A sufficient estimator captures all the useful information from the data.

Invariance (Property of Love)

If the function of the parameter changes, the estimator also changes with some functional applications. This property is known as invariance.

\begin{align}
E(X-\mu)^2 &= \sigma^2 \\
\text{or } \sqrt{E(X-\mu)^2} &= \sigma\\
\text{or } [E(X-\mu)^2]^2 &= (\sigma^2)^2
\end{align}

The property states that if $\hat{\theta}$ is the MLE of $\theta$ then $\tau(\hat{\theta})$ is the MLE of $\tau(\hat{\theta})$ for any function. The Taw ($\tau$) is the general form of any function. for example $\theta=\overline{X}$, $\theta^2=\overline{X}^2$, and $\sqrt{\theta}=\sqrt{\overline{X}}$.

From the above diagrammatic representations, one can visualize the properties of a good estimator as described below.

• Unbiasedness: The estimator should be centered around the true value.
• Efficiency: The estimator should have a smaller spread (variance) around the true value.
• Consistency: As the sample size increases, the estimator should become more accurate.
• Sufficiency: The estimator should capture all relevant information from the sample.

In summary, regarding the properties of a good estimator, a good estimator is unbiased, efficient, consistent, and ideally sufficient. It should also be robust to outliers and have a low MSE.

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