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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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The...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

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Related Experiment Video

Updated: Jun 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

DPRESS: Localizing estimates of predictive uncertainty.

Robert D Clark1

  • 1Biochemical Infometrics, 827 Renee Lane, Creve Coeur MO 63141, USA. bclark@bcmetrics.com.

Journal of Cheminformatics
|March 20, 2010
PubMed
Summary
This summary is machine-generated.

Estimating prediction uncertainty for QSAR models is crucial. The Distributed PRedictive Error Sum of Squares (DPRESS) method provides reliable, individualized uncertainty estimates for new compounds based on their proximity to training data.

Related Experiment Videos

Last Updated: Jun 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Quantitative Structure-Activity Relationship (QSAR) modeling
  • Computational chemistry
  • Cheminformatics

Background:

  • Increasing need for quantitative prediction uncertainty in QSAR models.
  • Classical statistical assumptions of independent and identically distributed (IID) errors are often violated.
  • Heteroscedasticity in QSAR models requires individualized predictive uncertainty estimates.

Purpose of the Study:

  • To introduce and validate the Distributed PRedictive Error Sum of Squares (DPRESS) method for estimating QSAR prediction uncertainty.
  • To address the challenge of inhomogeneous error (heteroscedasticity) in QSAR models.
  • To provide a method for estimating individual predictive uncertainty for new compounds.

Main Methods:

  • DPRESS estimates predictive uncertainty (su) using the non-cross-validated error (st*) of the closest training set object (t*).
  • Adjustment is made for the distance (d) between the new object (u) and t* in descriptor space, relative to training set size.
  • The predictive uncertainty factor (gammat*) is derived by distributing the internal predictive error sum of squares based on inter-object distances.

Main Results:

  • DPRESS was applied to partial least-squares models using 2D or 3D descriptors with mid-sized training sets (N=75).
  • Good qualitative and quantitative agreement was observed between DPRESS uncertainty estimates and actual predictive errors on external test sets (N=229).
  • The distance-dependent term in DPRESS was essential for accurate agreement; estimates were conservative even with biased training sets.

Conclusions:

  • DPRESS offers a straightforward and powerful method for reliably estimating individual predictive uncertainties for external compounds.
  • The method utilizes the distance to the nearest neighbor in the training set and its associated internal predictive uncertainty.
  • DPRESS provides a sample-based, a posteriori approach to defining applicability domains based on localized uncertainty.