24 papers · ranked by Valyu relevance
Jia Cong, Zubair Ahmad, Basim S. O. Alsaedi, Osama Abdulaziz Alamri + 2 more
'Ibrahim Alkhairy' 'Hassan Alsuhabi'] Marketing refers to the strategies a company undertakes to promote its brands to its potential audience. Advertising provides useful venues for marketing to promote a company's survives/goods to the audience. It has a positive impact on the sale of services or products. In this…
Beate Jahn, Sarah Friedrich, Joachim Behnke, Joachim Engel + 8 more
'Ursula Garczarek' 'Ralf Münnich' 'Markus Pauly' 'Adalbert Wilhelm' 'Olaf Wolkenhauer' 'Markus Zwick' 'Uwe Siebert' 'Tim Friede'] A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which…
Michele Bennett, Karin Hayes, Ewa J. Kleczyk, Rajesh Mehta
Data scientists and statisticians are often at odds when determining the best approach – machine learning or statistical modeling – to solve an analytics challenge. However, machine learning and statistical modeling are more cousins than adversaries on different sides of an analysis battleground. Choosing between the…
Michael Dumelle, Matt Higham, Jay M. Ver Hoef, A. K. M. Anisur Rahman
'A. K. M. Anisur Rahman'] spmodel is an R package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms.…
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…
Greenland, Sander
The study of associations and their causal explanations is a central research activity whose methodology varies tremendously across fields. Even within specialized subfields, comparisons across textbooks and journals reveals that the basics are subject to considerable variation and controversy. This variation is often…
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…
Xuming He, David Madigan, Bin Yu, Jon Wellner
| EXECUTIVE SUMMARY | 4 | | --- | --- | | SECTION 1: ROLE/VALUE OF STATISTICS AND DATA SCIENCE | 6 | | SECTION 2: CHALLENGES IN SCIENTIFIC AND SOCIAL APPLICATIONS | 10 | | SECTION 3: FOUNDATIONAL RESEARCH | 16 | | SECTION 4: PROFESSIONAL CULTURE & COMMUNITY RESPONSIBILITIES | 20 | | SECTION 5: DOCTORAL EDUCATION | 23 |…
Joshua P. Jahner, C. Alex Buerkle, Dustin G. Gannon, Eliza M. Grames + 11 more
The proliferation of biological data with large numbers of samples and many dimensions is kindling hope that life scientists will be able to fit statistical and machine learning models that are highly predictive and interpretable. However, large biological data sets are commonly burdened with an inherent trade-off…
Atef F. Hashem, M. A. Abdelkawy, Abdisalam Hassan Muse, Haitham M. Yousof
'Haitham M. Yousof'] The current study introduces and examines copula-coupled probability distributions. It explains their mathematical features and shows how they work with real datasets. Researchers, statisticians, and practitioners can use this study’s findings to build models that capture complex multivariate data…
Russell J. Bowater
In using observed data to make inferences about a population quantity, it is commonly assumed that the sampling distribution from which the data were drawn belongs to a given parametric family of distributions, or at least, a given finite set of such families, i.e. the population space is assumed to be closed. Here, we…
Eliuvish Han Cui
This monograph develops probability and stochastic-process ideas as a translation language for statistics: from designed observations and data objects to targets, stability statements, inference, and use. The chapters move from motivating examples and randomization through probability measures, kernels, likelihoods…
Rex Parsons, Oliver Jayasinghe, Nicole White, Prasad Chunduri + 1 more
The complexity, volume, and importance of time series data across various research domains highlight the necessity for tools that can efficiently analyze, visualize, and extract insights. Cosinor modeling is a widely used methodology to estimate or compare rhythmic characteristics in time series datasets. Time series…
Satwik Acharyya, Debdeep Pati, Dipankar Bandyopadhyay, Shumei Sun
Beta distributions are commonly used to model proportion valued response variables, commonly encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a…
Dan Tulpan, Luis O Tedeschi, Hector Menendez, Ricardo Augusto M Vieira
Integrating open-source tools and machine learning (ML) pipelines into livestock data analysis transforms research, education, and decision-making in animal science. This study presents a comprehensive, end-to-end regression pipeline implemented in Python, designed to predict outcome variables from structured input…
Andrew Gelman, Keith O’Rourke, Carlos Alberto De Bragança Pereira, Paulo Canas Rodrigues + 1 more
'Paulo Canas Rodrigues' 'Mark Andrew Gannon'] Amalgamation of evidence in statistics is conducted in several ways. Within a study, multiple observations are combined by averaging, or as factors in a likelihood or prediction algorithm. In multilevel modeling or Bayesian analysis, population or prior information is…
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…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Radu V. Craiu, Ruobin Gong, Xiao-Li Meng
This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a "sense" because we think of these as essential in helping every statistical mind connect in constructive and insightful ways with statistical theory, methodologies, and…
Luis F. Arias-Giraldo, Marlon E. Cobos
Here, we present the new R package “enmpa,” which includes a range of tools for modeling ecological niches using presence-absence data via logistic generalized linear models. The package allows users to calibrate, select, project, and evaluate models using independent data. We have emphasized a comprehensive search for…
Vladimir Vovk, Glenn Shafer
Beginning in the 1970s, Alexander Philip Dawid has been a leading contributor to the foundations of statistics and especially to the development and application of Bayesian statistics. He is also known for his work on causality, especially his notation for conditional independence and his critique of the overuse of…
James Wellnitz, Sankalp Jain, Joshua Hochuli, Travis Maxfield + 3 more
Traditional best practices for Quantitative Structure Activity Relationship (QSAR) modeling recommend dataset balancing and balanced accuracy (BA) as the key desired objective of model development. This study challenges the conventional norms by recommending the use of models with the highest positive predictive value…
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…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…