25 papers · ranked by Valyu relevance
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Ben Klemens
This paper proposes a single form for statistical models that accommodates a broad range of models, from ordinary least squares to agent-based microsimulations. The definition makes it almost trivial to define morphisms to transform and combine existing models to produce new models. It offers a unified means of…
Rick Farouni
In general, we will be limiting our focus mainly to the the multivariate statistics setting in which we treat an observation as a multivariate random vector yn = (yn,1, . . . , yn,P ) consisting of P features and the data as a set of N observations y = {y1, · · · , yn, · · · , yN }. Accordingly, we can think of the…
Tanya N Beran, Claudio Violato
Background Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables. Similar but more powerful than regression analyses, it examines linear causal relationships among variables, while simultaneously accounting for measurement…
Christian Hennig
Most statisticians are aware that probability models interpreted in a frequentist manner are not really true in objective reality, but only idealisations. I argue that this is often ignored when actually applying frequentist methods and interpreting the results, and that keeping up the awareness for the essential…
Eunice Jun, Audrey Seo, Jeffrey Heer, René Just
Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Michael P.H. Stumpf
The development of mathematical models of biological systems has largely relied on a mix of biological intuition, mathematical expediency, and comparisons with data. Models are hard to develop and hard to validate. Recent progress in theoretical systems biology, applied mathematics and computational statistics has…
Helen E. Clough, Gemma L. Chaters, Arie H. Havelaar, K. Marie McIntyre + 8 more
'K. Marie McIntyre' 'Thomas L. Marsh' 'Ellen C. Hughes' 'Wudu T. Jemberu' 'Deborah Stacey' 'Joao Sucena Afonso' 'William Gilbert' 'Kassy Raymond' 'Jonathan Rushton'] Livestock provide nutritional and socio-economic security for marginalized populations in low and middle-income countries. Poorly-informed decisions…
Teegwendé V. Porgo, Susan L. Norris, Georgia Salanti, Leigh F. Johnson + 4 more
'Leigh F. Johnson' 'Julie A. Simpson' 'Nicola Low' 'Matthias Egger' 'Christian L. Althaus'] Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision-making, particularly when data from systematic reviews of primary studies do…
L. Mark Berliner, Radu Herbei, Christopher K. Wikle, Ralph F. Milliff + 1 more
'Ralph F. Milliff' 'Pablo Martin Rodriguez'] Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We…
Aaditya Prasad Gupta
Biological systems, at all scales of organization from nucleic acids to ecosystems, are inherently complex and variable. Therefore mathematical models have become an essential tool in systems biology, linking the behavior of a system to the interaction between its components. Parameters in empirical mathematical models…
Authors not listed
Within the biological, physical, and social sciences, there are two broad quantitative traditions: statistical and mathematical modeling. Both traditions have the common pursuit of advancing our scientific knowledge, but these traditions have developed largely independently using distinct languages and inferential…
Roemer J Janse, Ameen Abu-Hanna, Iacopo Vagliano, Vianda S Stel + 5 more
'Kitty J Jager' 'Giovanni Tripepi' 'Carmine Zoccali' 'Friedo W Dekker' 'Merel van Diepen'] Title: ABSTRACT An artificial intelligence boom is currently ongoing, mainly due to large language models, leading to significant interest in artificial intelligence and subsequently also in machine learning (ML). One area where…
Georg Manthey, Miriam Liedvogel, Birgen Haest, Michael Manthey + 1 more
The ability to select statistical models based on how well they fit an empirical dataset is a central tenet of modern bioscience. How well this works, though, depends on how goodness-of-fit is measured. Likelihood and its derivatives (e.g. AIC) are popular and powerful tools when measuring goodness-of-fit, though…
Jeffrey A. Walker
Model-averaged partial regression coefficients have been criticized for averaging over a set of models with coefficients that have different meanings from model to model. This criticism arises because statisticians since Fisher believe that the meaning of a coefficient in a regression model arises from probabilistic…
Lihan Yan, Yongmin Sun, Michael R. Boivin, Paul O. Kwon + 1 more
This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of outliers, (b)…
James S. Cavenaugh
Cross-validation assesses the predictive ability of a model, allowing one to rank models accordingly. Although the nonparametric bootstrap is almost always used to assess the variability of a parameter, it can be used as the basis for cross-validation if one keeps track of which items were not selected in a given…
Colin D. Kinz-Thompson, Korak Kumar Ray, Ruben L. Gonzalez
Biophysics experiments performed at single-molecule resolution contain exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. Here, we discuss how to…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Conrad Hübler
A novel application to determine stability constants from supramolecular titration experiments is presented. The focus lies on NMR titration and ITC experiments for pure 1:1 systems, as well as mixed 2:1/1:1, 1:1/1:2 and 2:1/1:1/1:2 systems. SupraFit provides global and local fitting and a global search tool.…
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…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Muhammad Farman, Muhammad Farhan Tabassum, Muhammad Saeed, Nazir Ahmad Chaudhry
Hepatitis B is the main public health problem of the whole world. In epidemiology, mathematical models perform a key role in understanding the dynamics of infectious diseases. This paper proposes Padé approximation (Pa) with Differential Evolution (DE) for obtaining solution of Hepatitis-B model which is nonlinear…
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…