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Related Concept Videos

Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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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Choosing Between z and t Distribution01:25

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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An R-Based Landscape Validation of a Competing Risk Model
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Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

Aaron J Baraff1, Tyler H McCormick1,2, Adrian E Raftery3,2

  • 1Department of Statistics, University of Washington, Seattle, WA 98195-4322.

Proceedings of the National Academy of Sciences of the United States of America
|December 9, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new tree bootstrap method to accurately assess uncertainty in respondent-driven sampling (RDS) estimates. This method addresses the high sampling variability often seen in RDS, improving data reliability for hard-to-reach populations.

Keywords:
HIVhard-to-reach populationinjecting drug usersnowball samplingsocial network

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Area of Science:

  • Social Sciences
  • Statistics
  • Epidemiology

Background:

  • Respondent-driven sampling (RDS) is a popular network-based survey method for hard-to-reach populations.
  • Existing statistical methods for RDS often underestimate sampling variability, leading to misleadingly narrow confidence intervals.
  • The statistical properties and uncertainty estimation for RDS remain significant challenges.

Purpose of the Study:

  • To introduce a novel tree bootstrap method for estimating uncertainty in respondent-driven sampling (RDS) estimates.
  • To demonstrate the effectiveness of the tree bootstrap method in capturing high sampling variability inherent in RDS.
  • To provide a reliable method for estimating uncertainty in RDS, applicable to various population attributes.

Main Methods:

  • Developed a tree bootstrap method based on resampling recruitment trees from RDS data.
  • Utilized simulations on known social networks to evaluate the tree bootstrap method's performance.
  • Applied the tree bootstrap method to real-world RDS data from injecting drug users in Ukraine.

Main Results:

  • The tree bootstrap method significantly outperforms existing methods in estimating uncertainty for RDS.
  • The method accurately captures high sampling variability, even in extreme cases with high design effects.
  • The tree bootstrap method's accuracy is independent of the attributes being measured, allowing for correlation estimation.

Conclusions:

  • The tree bootstrap method offers a robust and accurate approach to assessing uncertainty in respondent-driven sampling.
  • This method enhances the reliability of RDS estimates, particularly for populations with complex network structures.
  • Accurate uncertainty assessment is crucial for the valid interpretation and application of RDS findings.