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Related Concept Videos

Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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What are Estimates?01:06

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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. 
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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.
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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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A New Monte Carlo Method for Estimating Marginal Likelihoods.

Yu-Bo Wang1, Ming-Hui Chen1, Lynn Kuo1

  • 1Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.

Bayesian Analysis
|May 29, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a novel class of Monte Carlo estimators for Bayesian model selection, offering improved consistency and theoretical properties over existing harmonic mean and inflated density ratio methods for marginal likelihood estimation.

Keywords:
Bayesian model selectioncure rate modelharmonic mean estimatorinflated density ratio estimatorordinal probit regressionpower prior

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Bayesian analysis relies on marginal likelihood for model selection.
  • Existing estimators like harmonic mean and inflated density ratio have limitations.

Purpose of the Study:

  • To propose a new class of Monte Carlo estimators for marginal likelihood.
  • To generalize existing methods using a partition weighted kernel.
  • To demonstrate improved theoretical and empirical performance.

Main Methods:

  • Developed a new class of Monte Carlo estimators.
  • Utilized a single Markov chain Monte Carlo sample from the posterior distribution.
  • Employed a partition weighted kernel (likelihood times prior).

Main Results:

  • The proposed estimator is consistent and theoretically superior.
  • Guidelines for optimal weight selection are provided.
  • Simulation studies and real data analysis confirm performance.

Conclusions:

  • The new estimator offers a robust and improved approach to marginal likelihood estimation in Bayesian analysis.
  • Applicable to complex models like ordinal probit and cure rate survival models.