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Related Concept Videos

Bootstrapping01:24

Bootstrapping

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 small or...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Effects of parameter estimation on maximum-likelihood bootstrap analysis.

Jennifer Ripplinger1, Zaid Abdo, Jack Sullivan

  • 1Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844-3017, USA. jiripplinger@gmail.com

Molecular Phylogenetics and Evolution
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

Maximum-likelihood bootstrap analysis is commonly used for phylogenetic trees. Simplifying this process, like fixing model parameters, may alter bootstrap values but likely not biological interpretations.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Bipartition support in maximum-likelihood (ML) phylogenetic analyses is typically assessed using the nonparametric bootstrap method.
  • Standard bootstrap procedures often deviate from theoretical ideals, omitting model selection and fixing parameters for computational efficiency.

Purpose of the Study:

  • To investigate the impact of parameter estimation and search strategies on ML bootstrap analysis.
  • To determine if simplified bootstrap methods affect the biological interpretation of phylogenetic trees.

Main Methods:

  • The study assessed the effects of forgoing model selection and fixing substitution-model parameters to maximum-likelihood estimates (MLEs) on bootstrap values.
  • It also evaluated the impact of less rigorous heuristic search strategies, including reduced branch swapping, on bootstrap results.

Main Results:

  • While omitting model selection or fixing parameters to empirical MLEs can significantly alter bootstrap values, biological interpretations remain largely unchanged.
  • Reduced search methods also yield different bootstrap values, but only omitting branch swapping substantially impacts biological inferences.

Conclusions:

  • Simplified ML bootstrap procedures, while affecting numerical support values, generally do not compromise the biological interpretation of phylogenetic trees.
  • The findings suggest that computational shortcuts in bootstrapping are often acceptable for inferring evolutionary relationships.