types of estimators with examples and applications

Estimating is an important part of any successful project. It requires accurate projections of resources, cost, and time needed to complete a task. Estimators are used to help project managers create a realistic timeline and budget for a project. Estimators come in a variety of forms and can be applied in many different contexts. In this blog post, we’ll look at the different types of estimators out there, provide examples, and examine applications where estimators are used. By understanding the various types of estimators and their applications, project managers and other professionals involved in project management can make better decisions and optimize their projects. So, let’s dive right in and explore the different types of estimators and their applications.

Estimators – the basics

Point Estimation vs. Interval Estimation

Point estimators and interval estimators are the two primary categories of estimators in statistics. Point estimation is the opposite of interval estimation. While the latter generates a variety of values, it only generates a single value.

A point estimator is a statistic used to calculate the value of a population’s unknowable parameter. When determining a single statistic that will be the most accurate estimation of the population’s unknowable parameter, it makes use of sample data.

Contrarily, interval estimation determines the range of potential values for a population’s unknown parameter using sample data. The parameter’s interval, also known as the confidence interval, is chosen so that it has a 95% or higher probability of occurring.

The confidence interval, which is derived from the observed data, is used to show how reliable an estimate is. The upper and lower confidence limits are used to describe the interval’s endpoints.

Point Estimation

Finding a single number from the sample that will represent the parameter’s unknown value is the goal of point estimation. The corresponding sample statistics (sample mean, variance, etc.) are used to estimate population parameters (population mean, variance, etc.). Point estimators, also known as estimators, are statistics that are used to estimate parameters. An estimate is the actual numerical value that an estimator produces. A population parameter is denoted by the undefined constant $theta$. The information is presented as a random sample of size $x_1, $x_2, $cdots, $x_n$ taken from the population. We formulate a function of the sample observation $x_1,x_2,cdots,x_n$. The estimator of $theta$ is denoted by $hat{theta}$. Different random samples yield various statistics ($hattheta$) values. Since $hattheta$ has its own sampling probability distribution, it is a random variable.


Several statistical “ingredients” are required for a particular model in order to use the estimator. The first is a statistical sample, which is a collection of data points taken from an N-dimensional random vector (RV). Put into a vector,.

Secondly, there are M parameters

whose values are to be estimated. Third, the probability mass function (pmf) or continuous probability density function (pdf) of the underlying distribution that produced the data must be expressed as a function of the parameter values:

The parameters themselves could also have a probability distribution (e g. , Bayesian statistics). It is then necessary to define the Bayesian probability.

After the model is formed, the goal is to estimate the parameters, with the estimates commonly denoted θ ^ {displaystyle {hat {mathbf {theta } }}} {hat {mathbf {theta } }}, where the “hat” indicates the estimate.

The minimum mean squared error (MMSE) estimator is a popular estimator that makes use of the discrepancy between the estimated and actual values of the parameters.

as the basis for optimality. The expected value of this squared value is then minimized for the MMSE estimator after this error term has been squared.

Examples of Estimation in Statistics

Depending on the type of study being conducted, different examples of estimation in statistics will be used. When calculating the number of sick days that its employees have taken, an employer is only permitted to make a point estimate. But when comparing the number of absences over a period of time, that employer may also use interval estimates. This information can be used to identify potential trends for the company. For instance, if there is a noticeable increase in absenteeism at a particular time, it can be found and corrected. This can assist a business in reassessing any established procedures they may have in place to reduce the number of absences.

Confidence intervals or interval estimates assist in making decisions regarding the use of medications, home sale or purchase prices, and the purchase of vehicles based on their safety features. In the real world, statistics is frequently applied to serve and enhance concepts or ideas as a whole.

The process of computing statistics using a sample size drawn from a population is known as estimation in statistics. As a result, the estimator is a random variable and may not be equivalent to the population parameter because the value is derived from the sample. In order to approximate the measured population parameter, this estimation is used. There are two types of statistics: inferential and descriptive statistics. Inferential statistics will compare the validity of each hypothesis using studies of a control or placebo group and potential effects on an individual.

In the study of statistics, point estimates and interval estimates are both used, and each has a distinct function depending on the data an analyst is attempting to uncover. A single numerical value from a parameter, usually the mean, will be used for point estimation. Interval estimates will lie between two points from which it is possible to derive a reasonable value.

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To ascertain the actual value of an object or function that could be found in a population, estimation in statistics is used. Consequently, determining this truth value might be made easier by using a sample of the population.

Any statistical procedure used to determine the value of a population derived from observations within a sample size derived from that population is known as an estimation. Point estimation and interval estimation are the two different types of estimation.

Any type of point estimation that can be applied to a population parameter is an example of estimation in statistics. As a result, the sample mean, sample variance, and sample standard deviation can all be used as parameters to determine the population sample.


What are the types of estimators?

Point estimators and interval estimators are the two primary categories of estimators in statistics. Point estimation is the opposite of interval estimation. While the latter generates a variety of values, it only generates a single value.

What is estimator and explain its types?

An estimator in statistics is a rule for estimating a given quantity based on observed data; as a result, the rule (the estimator), the quantity of interest (the estimand), and its outcome (the estimate) are distinguished. For instance, a frequently employed estimator of the population mean is the sample mean.

What are the two types of estimators?

There are two types of estimates: point and interval. When estimating a population parameter, a point estimate is a value from a sample statistic that is used once.

What is the application of estimation?

Applications for estimation In signal processing, estimation is used to approximate an unobserved signal using an observed signal that contains noise. Forecasting and prediction are used to estimate future observed quantities.

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