21 papers · ranked by Valyu relevance
Sonya M. Hanson, Sean Ekins, John D. Chodera
All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate…
Stefanie Muff, Andrea Riebler, Håvard Rue, Philippe Saner + 1 more
'Leonhard Held'] Summary. To account for Measurement error (ME) in explanatory variables, Bayesian approaches provide a flexible framework, as expert knowledge about unobserved covariates can be incorporated in the prior distributions. However, given the analytic intractability of the posterior distribution, model…
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…
Stephanie L. Bailey, Rose S. Bono, Denis Nash, April D. Kimmel + 1 more
We classified and characterized unintentional errors identified in Eqs (11-3)$1$-(11-4)$3$. Unintentional errors were classified as due to: incorrect cell names, incorrect cell references, incorrect range(s) in a formula, incorrectly copied formulae, overwritten formulae, and misuse of built-in functions . For each…
Serena Arima, Silvia Polettini
Model-based small area estimation relies on mixed effects regression models that link the small areas and borrow strength from similar domains. When the auxiliary variables used in the models are measured with error, small area estimators that ignore the measurement error may be worse than direct estimators.…
Raghul Parthipan, Mohit Anand, Hannah M. Christensen, J. Scott Hosking + 1 more
'J. Scott Hosking' 'Damon Wischik'] Machine learning (ML) has recently shown significant promise in modelling atmospheric systems, such as the weather. Many of these ML models are autoregressive, and error accumulation in their forecasts is a key problem. However, there is no clear definition of what 'error…
Erica Ponzi, Lukas F. Keller, Stefanie Muff
Measurement error and other forms of uncertainty are commonplace in ecology and evolution and may bias estimates of parameters of interest. Although a variety of approaches to obtain unbiased estimators are available, these often require that errors are explicitly modeled and that a latent model for the unobserved…
You Wu, F. Owen Hoffman, A. Iulian Apostoaei, Deukwoo Kwon + 3 more
'Brian A. Thomas' 'Racquel Glass' 'Lydia B. Zablotska'] Background Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Brock D. Sherlock, Marko A. A. Boon, Maria Vlasiou, Adelle C. F. Coster
Measurement error is an unavoidable feature of experimental data collection. It is common in mathematical biology to consider measurement error in the dependent variable. However, less attention has been given to errors in the independent variable. This work is focussed on the effects of independent variable…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
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…
Dylan Spicker, Michael P. Wallace, Grace Y. Yi
Measurement error is a pervasive issue which renders the results of an analysis unreliable. The measurement error literature contains numerous correction techniques, which can be broadly divided into those which aim to produce exactly consistent estimators, and those which are only approximately consistent. While…
Zhezheng Ren, Xuzhe Xia, Yuzhi Tang, Bo Zhao + 2 more
We present a comparative analysis of two distinct machine-learning models designed to detect asynchronous errors during Human-Robot Interaction (HRI). The models under scrutiny are a customized ResNet model and an ensemble model, both trained and validated using EEG data. The ResNet model is a unique adaptation of the…
Authors not listed
Moving bed reactors (MBRs) are widely used in various industrial processes, making the development of mathematical models crucial for their design, optimization, and control. This study presents a semi-analytical solution (SAS) for a lumped parameter kinetic and heat transfer model of a tubular MBR, where a first-order…
Authors not listed
Material properties calculated using density functional theory (DFT) are often corrected to more closely match experimental values, but the most common correction method has flaws that lead to unphysical results and false positives in the material discovery process. In this work, we show that these flaws stem from the…
Emma Sofie Skarstein, Leonardo Soares Bastos, Håvard Rue, Stefanie Muff
nested Laplace approximations Authors: ['Emma Sofie Skarstein' 'Leonardo Soares Bastos' 'Håvard Rue' 'Stefanie Muff'] Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for…
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…
Sunmee Kim, Heungsun Hwang
Extended redundancy analysis (ERA) is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid…
Ping Chen, Robert J. Bauer, Yan Li
Population pharmacokinetic (popPK) models are commonly developed using ordinary differential equations (ODEs) to describe deterministic concentration–time profiles, with unexplained variability typically attributed to interindividual variability or residual error. When model misspecification is present, system-level…
Authors not listed
There is an error in the PDF version of the published article. In the Material and Methods, under the subsection Experimental design and statistical analysis, the text should indicate the following: F 1 = Model / Total error F 1 ≥ F d e n n u m The Publisher apologizes for the error.