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Related Concept Videos

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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Estimating Population Mean with Unknown Standard Deviation01:22

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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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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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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Estimating Population Mean with Known Standard Deviation01:16

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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.
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(point estimate - error bound, point estimate +...
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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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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Hidden Population Estimation with Indirect Inference and Auxiliary Information.

Justin Weltz1, Eric Laber1,2, Alexander Volfovsky1,3

  • 1Department of Statistical Science, Duke University, Durham, North Carolina, USA.

Proceedings of Machine Learning Research
|September 22, 2025
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Summary
This summary is machine-generated.

Respondent Driven Sampling (RDS) struggles with accurate hidden population size estimation. This study introduces a new method using auxiliary data and indirect inference to reduce bias and improve precision in RDS surveys.

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Area of Science:

  • Social Network Analysis
  • Statistical Modeling
  • Public Health Research

Background:

  • Conventional survey methods face challenges in sampling hidden or stigmatized populations.
  • Respondent Driven Sampling (RDS) is a key method for reaching these groups, but existing imputation techniques introduce bias.
  • Accurate estimation of hidden population sizes is crucial for public health interventions.

Purpose of the Study:

  • To develop an improved statistical method for estimating hidden population sizes using Respondent Driven Sampling (RDS).
  • To address and reduce estimation bias inherent in current RDS imputation techniques.
  • To enhance the precision of key RDS-derived metrics, including arrival rates and subgraph characteristics.

Main Methods:

  • Modeling RDS as a stochastic process on social network graphs.
  • Leveraging auxiliary participant information and indirect inference for improved imputation.
  • Developing novel statistical techniques to correct for biased edge imputation in RDS.

Main Results:

  • The proposed method significantly reduces bias in estimating the study participant arrival rate, sample subgraph, and overall population size.
  • Improved precision was observed in key estimation parameters compared to existing methods.
  • Demonstrated successful application in estimating the size of the People Who Inject Drugs (PWID) population in Estonia.

Conclusions:

  • The novel indirect inference approach offers a more accurate and precise way to analyze RDS data.
  • This method enhances the reliability of estimates for hidden populations, crucial for targeted health programs.
  • The findings have direct implications for improving the accuracy of public health surveys in hard-to-reach populations.