25 papers · ranked by Valyu relevance
Alexei Botchkarev
Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge…
Aryan Jadon, Avinash Patil, Shruti Jadon
—Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance. Numerous publications published in the last five years have proposed diverse sets of objective loss functions to address…
Clintin P. Davis-Stober, Jason Dana, Jeffrey N. Rouder, Alan D Hutson
'Alan D Hutson'] Sample means comparisons are a fundamental and ubiquitous approach to interpreting experimental psychological data. Yet, we argue that the sample and effect sizes in published psychological research are frequently so small that sample means are insufficiently accurate to determine whether treatment…
Scott M. Robeson, Cort J. Willmott, Fabiana Zama
When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean…
Umberto Michelucci, Francesca Venturini
- In this paper, formulas for the expectation value of the metrics MSE, MAE and accuracy that keeps into account measurement errors on the labels in supervised learning are derived. - Formulas for the variance of the metrics MSE, MAE and accuracy that keeps into account measurement errors on the labels in supervised…
Prayas Sharma, Rajesh Singh
In this paper, we have proposed a new Ratio Type Estimator using auxiliary information on two auxiliary variables based on Simple random sampling without replacement (SRSWOR). The proposed estimator is found to be more efficient than the estimators constructed by Olkin (1958), Singh (1965), Lu (2010) and Singh and…
Husam Abdulnabi, J. Timothy Westwood
A quantitative measurement can have variation, referred to here as measurement variation, which is a probability distribution. Machine Learning models typically produce a prediction corresponding to the mode of the measurement variation. The Deviation Error is a novel metric, described here, to assess predictions that…
Aaron Caldwell, Andrew D. Vigotsky, Amador García-Ramos
Recent discussions in the sport and exercise science community have focused on the appropriate use and reporting of effect sizes. Sport and exercise scientists often analyze repeated-measures data, from which mean differences are reported. To aid the interpretation of these data, standardized mean differences (SMD) are…
A. Nicholls
Computational chemistry is a largely empirical field that makes predictions with substantial uncertainty. And yet the use of standard statistical methods to quantify this uncertainty is often absent from published reports. This article covers the basics of confidence interval estimation for molecular modeling using…
Husam Abdulnabi, J. Timothy Westwood
A quantitative measurement can have variation, referred to here as measurement variation, which is a probability distribution. Machine Learning models typically produce a prediction corresponding to the mode of the measurement variation. The Deviation Error is a novel metric, described here, to assess predictions that…
Authors not listed
LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming.…
Simone Lederer, Tjeerd M. H. Dijkstra, Tom Heskes
High-throughput techniques allow for massive screening of drug combinations. To find combinations that exhibit an interaction effect, one filters for promising compound combinations by comparing to a response without interaction. A common principle for no interaction is Loewe Additivity which is based on the assumption…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Branimir Zauner, Branko Petrinec, Tomislav Bituh, Saša Ceci + 4 more
'Nikola Volarić' 'Aleksandar Včev' 'Andrea Vukoja' 'Dinko Babić'] Title: Abstract We present an overview of the theory of random measurement errors, focusing on the underlying concepts rather than on a strict mathematical formulation. Although the related literature is extensive, one can frequently encounter partly or…
Authors not listed
Bonkowski and De Souza [Sol. Stat. Ionics 429, 116967 (2025)] provide a guide for performing molecular dynamics simulations of ion transport, including methods for estimating diffusion coefficients and their uncertainties from mean-squared displacement (MSD) data. The discussion of uncertainty in estimated diffusion…
Xiaoming Ye
- Variance is the evaluation of probability interval of an error, instead of the dispersion of measured value defined by the existing measurement theory. The dispersion of measured value is 0. - Variance is expressed by the dispersion of all possible values of error. - All possible values refer to the test values under…
Alice Allen, Michael J. Robertson, Michael C. Payne, Daniel Cole
Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules, and thus they neglect system-specific polarization. In this paper, we introduce a complete protein force field that is designed to be compatible with the…
Elena Kulinskaya, David C. Hoaglin
Meta-analysis aims to combine effect measures from several studies. For continuous outcomes, the most popular effect measures use simple or standardized differences in sample means. However, a number of applications focus on the absolute values of these effect measures (i.e., unsigned magnitude effects). We provide…
Alexander K. Nussbaum, Richard Seides
Statistics is one of the most valuable of disciplines. Science is based on proof and it alone produces results, other approaches are not, and do not. Statistics is the only acceptable language of proof in science. Yet statistics is difficult to understand for a large percentage of those who will be evaluating and even…
Guanghui Zhang, Steven J. Luck
Although it is widely accepted that data quality for event-related potential (ERP) components varies considerably across studies and across participants within a study, ERP data quality has not received much systematic analysis. The present study used a recently developed metric of ERP data quality— the standardized…
Kalina Hristova, William C. Wimley
We present a simple, spreadsheet-based method to determine the statistical significance of the difference between any two arbitrary curves. This modified Chi-squared approach includes a critical correction for the deviation from normality in measurements with small sample size, which are typical in biomedical sciences.…
Authors not listed
Development of meta-generalized gradient approximations (meta-GGAs) has generally led to more accurate density-functional approximations, albeit ones that have more stringent requirements for the quadrature grids that are used to evaluate the exchange-correlation energy. Here, we demonstrate that grid-induced errors…
Linda Nab, Maarten van Smeden, Ruth H. Keogh, Rolf H. H. Groenwold
1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands 2Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands 3Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom 4Department…