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Weighted Mean00:57

Weighted Mean

6.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.6K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.8K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

940
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
940
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.3K
What are Estimates?01:06

What are Estimates?

7.6K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
7.6K

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Related Experiment Video

Updated: Nov 27, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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A Data-Weighted Prior Estimator for Forecast Combination.

Esteban Fernández-Vázquez1, Blanca Moreno1, Geoffrey J D Hewings2

  • 1REGIOlab and Department of Applied Economics, University of Oviedo, Faculty of Economics and Business, Avda. del Cristo, s/n, 33006 Oviedo, Spain.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

Entropy econometrics offers a novel approach to forecast combination, effectively distinguishing between good and bad forecasters using the data-weighted prior (DWP) estimator. This method outperforms traditional techniques like simple averages, even with limited data.

Keywords:
combined forecastdata-weighted priorgeneralized maximum entropy method

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Information Theory

Background:

  • Forecast combination methods aggregate multiple forecasts into one.
  • The simple arithmetic mean is a common but limited approach.
  • Existing methods struggle to differentiate forecaster quality with scarce data.

Purpose of the Study:

  • To introduce entropy econometrics for forecast combination.
  • To develop a method that identifies and utilizes high-performing forecasters.
  • To improve forecast accuracy, especially under data scarcity.

Main Methods:

  • Utilizing the data-weighted prior (DWP) estimator from Golan (2001).
  • Applying DWP for simultaneous parameter estimation and forecaster selection.
  • Testing the DWP estimator's ability to select relevant forecasts.

Main Results:

  • The DWP estimator effectively discriminates between good and bad forecasters.
  • The proposed entropy econometrics method shows superior performance.
  • Simulation exercises confirm the model's accuracy and effectiveness.

Conclusions:

  • Entropy econometrics provides a robust framework for forecast combination.
  • The DWP estimator offers significant advantages over traditional methods.
  • This approach enhances forecasting accuracy by selecting superior information sources.