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Related Concept Videos

Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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What are Estimates?01:06

What are Estimates?

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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...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.0K
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 +...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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...
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Related Experiment Video

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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An Efficient Estimator for the Expected Value of Sample Information.

Nicolas A Menzies1

  • 1Department of Global Health and Population and the Center for Health Decision Science, Harvard University, Boston, MA (NAM) nmenzies@hsph.harvard.edu.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|April 26, 2015
PubMed
Summary
This summary is machine-generated.

Novel algorithms efficiently estimate the expected value of sample information (EVSI) by reweighting prior distributions. This approach simplifies EVSI computation for complex models and various study designs, overcoming previous limitations.

Keywords:
EVSIdecision theoryresearch designvalue of information

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Area of Science:

  • Decision analysis
  • Health economics
  • Bayesian statistics

Background:

  • Conventional methods for estimating the expected value of sample information (EVSI) are computationally intensive and limited.
  • Existing techniques struggle with complex models and diverse study designs.

Purpose of the Study:

  • To introduce novel, computationally efficient algorithms for EVSI estimation.
  • To enable EVSI calculation across a broader range of study designs and complex models.

Main Methods:

  • Estimated posterior parameter distributions by reweighting prior draws.
  • Applied conventional probabilistic sensitivity analysis for EVSI calculation without further model evaluations.
  • Utilized smoothing techniques in Algorithm 2 for enhanced accuracy.
  • Compared Algorithm 1 and 2 against conventional (2-level Monte Carlo) and Brennan-Kharroubi (BK) estimators.

Main Results:

  • Algorithm 2 demonstrated 8%-17% lower root mean square error (RMSE) than conventional methods, requiring significantly fewer model evaluations.
  • Algorithms showed 18%-38% lower RMSE compared to the BK estimator in most scenarios, with substantial reductions in model evaluations.
  • Algorithm 1's accuracy varied with study evidence strength, underestimating EVSI in strong evidence scenarios.

Conclusions:

  • The proposed algorithms address key challenges in EVSI estimation, including posterior distribution calculation and the need for extensive model evaluations.
  • These novel methods make EVSI estimation more feasible and accessible for diverse research applications.
  • The findings facilitate more efficient decision-making in the presence of uncertainty.