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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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Standard Error of the Mean01:13

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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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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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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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An R-Based Landscape Validation of a Competing Risk Model
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Block-Regularized m × 2 Cross-Validated Estimator of the Generalization Error.

Ruibo Wang1, Yu Wang2, Jihong Li3

  • 1School of Software, Shanxi University, Taiyuan 030006, P.R.C. wangruibo@sxu.edu.cn.

Neural Computation
|December 29, 2016
PubMed
Summary
This summary is machine-generated.

K-fold cross-validation

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Area of Science:

  • Machine Learning
  • Statistical Learning Theory

Background:

  • K-fold cross-validation is standard for estimating generalization error.
  • Random data partitioning in k-fold CV can inflate variance.
  • This variance issue worsens with increasing k.

Purpose of the Study:

  • Introduce block-regularized k-fold cross-validation (BCV).
  • Reduce variance in generalization error estimation.
  • Provide a method for constructing BCV and estimating its variance.

Main Methods:

  • Define block-regularized partitions to restrict sample overlap.
  • Develop block-regularized k-fold cross-validation (BCV).
  • Prove variance reduction properties of BCV.
  • Utilize two-level orthogonal arrays for BCV construction.

Main Results:

  • BCV significantly reduces variance compared to standard k-fold CV.
  • Variance reduction is most pronounced under specific partitioning conditions.
  • An analytical variance expression is derived for optimal BCV.
  • Simulation experiments validate theoretical findings.

Conclusions:

  • BCV offers a more stable and reliable method for generalization error estimation.
  • The proposed construction and variance estimation methods are practical.
  • Block-regularized partitioning is a key improvement over random partitioning.