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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Goodness-of-Fit Test

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

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Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Related Experiment Video

Updated: May 21, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary

Benjamin W Domingue¹, Charles Rahal², Jessica Faul³

¹Graduate School of Education, Stanford University, Stanford, California, United States of America.

|March 21, 2025

Summary

We introduce the InterModel Vigorish (IMV), a new metric for assessing changes in predictive model accuracy for binary outcomes. The IMV offers consistent interpretation across different prevalence rates, aiding social science research.

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Published on: October 23, 2020

Area of Science:

Social Sciences
Statistical Modeling
Predictive Analytics

Background:

Quantifying model fit for binary outcome prediction is a persistent challenge in social sciences.
Existing metrics often require manipulation to compare model fit differences.

Purpose of the Study:

To introduce the InterModel Vigorish (IMV), a novel metric for measuring changes in predictive accuracy.
To provide a flexible, portable, and intuitive tool for assessing model fit.
To offer a metric consistently interpretable across varying baseline prevalence rates.

Main Methods:

Developed the InterModel Vigorish (IMV) based on an analogy to weighted coins.
Conducted simulations to contrast IMV with alternative metrics.
Applied IMV to examples from social and natural sciences.

Main Results:

The IMV quantifies the change in accuracy between two predictive systems for binary outcomes.
IMV is always a statement about change in fit relative to a baseline model.
IMV demonstrates consistent interpretability irrespective of baseline prevalence.
IMV shows greater sensitivity to estimation error and prevalence compared to some alternatives.

Conclusions:

The InterModel Vigorish (IMV) provides a precise and interpretable method for evaluating changes in model fit.
IMV is applicable across diverse scientific fields, enhancing research on social and natural outcomes.