27 papers · ranked by Valyu relevance
Wang, Jingyuan, Ji, Jiahao
This article serves as the regression analysis lecture notes in the Intelligent Computing course cluster (including the courses of Artificial Intelligence, Data Mining, Machine Learning, and Pattern Recognition) at the School of Computer Science and Engineering, Beihang University. It aims to provide students – who are…
Sidharth SS
CurvPy is an open-source Python library for automated curve fitting and regression analysis, aiming to make advanced statistical and machine learning techniques more accessible. This paper explores the mathematical foundations and implementation of key CurvPy components for optimization, smoothing, imputation…
Hooman H. Rashidi, Samer Albahra, Scott Robertson, Nam K. Tran + 1 more
'Bo Hu'] One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be…
David P. Hofmeyr
We consider a simple approach for approximating detailed information about the conditional distribution of a real-valued response variable, given values for its covariates, using only the outputs from a standard regression model. We validate this approach by assessing its performance in the context of quantile…
Akhila Henry, Nithin Nagaraj
This study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with traditional regression algorithms —- Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression (SVR). Our approach…
John B. Carlin, Margarita Moreno‐Betancur
Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasizes the details of regression models and methods ahead of the purposes for which such modeling might be useful. More broadly, statistics is widely understood to provide a body of techniques for “modeling data,”…
Chih-Ching Yeh, Yan Sun, Adele Cutler
Regression methods for interval-valued data have been increasingly studied in recent years. As most of the existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and therefore development of nonlinear regression tools for interval-valued data is crucial. In…
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…
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…
Lee Jones, Adrian Barnett, Dimitrios Vagenas, Mohamed R. Abonazel
This study was not designed to be an in-depth tutorial on linear regression but rather an overview so that the paper is accessible to non-statistical readers, for further reading on linear regression, see . Linear regression models explain the relationship between a dependent variable and one or more independent…
Fatih Gurcan, Carlos Fernandez-Lozano
Background The continuous increase in carbon dioxide (CO2) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle…
Abhishek Gupta, Raunak Joshi, Nandan Kanvinde, Pinky Gerela + 1 more
'Ronald Melwin Laban'] Regression branch of Machine Learning purely focuses on prediction of continuous values. The supervised learning branch has many regression based methods with parametric and non-parametric learning models. In this paper we aim to target a very subtle point related to distance based regression…
Tom Dupré la Tour, Michael Eickenberg, Anwar O. Nunez-Elizalde, Jack L. Gallant
Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential complementarity of different…
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Muhammad Aslam, Ali Hussein AL-Marshadi
In this paper, a new trimmed regression model under the neutrosophic environment is introduced. The mathematical model of the new regression model along with its neutrosophic form is given. The methods to find the error sum of square and trended values are also given. The trimmed neutrosophic correlation is also…
Matthew W. Linakis, Cynthia Van Landingham, Alessandro Gasparini, Matthew P. Longnecker
Meta-analysis poses a challenge when original study results have been expressed in a non-uniform manner, such as when regression results from some original studies were based on a log-transformed key independent variable while in others no transformation was used. Methods of re-expressing regression coefficients to…
Surojit Biswas, Buddhananda Banerjee
transformation Authors: ['Surojit Biswas' 'Buddhananda Banerjee'] Abstract. This paper introduces a novel regression model designed for angular response variables with linear predictors, utilizing a generalized M¨obius transformation to define the regression curve. By mapping the real axis to the circle, the model…
Thomas M. M. Versluys
1.Plastic traits, capable of taking multiple forms, often correlate with one another or with features of the environment when measured over time. These patterns of correlated change are sometimes assumed to reflect adaptive plasticity, such as coevolved “integrated phenotypes” within individuals, synchronisation…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…
Benjamin Planterose Jiménez, Manfred Kayser, Athina Vidaki, Amke Caliebe
Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this short-coming is usually resolved by complete-case analysis or…
Nicholas C Chesnaye, Merel van Diepen, Friedo Dekker, Carmine Zoccali + 2 more
'Carmine Zoccali' 'Kitty J Jager' 'Vianda S Stel'] Title: ABSTRACT True linear relationships are rare in clinical data. Despite this, linearity is often assumed during analyses, leading to potentially biased estimates and inaccurate conclusions. In this introductory paper, we aim to first describe-in a non-mathematical…
Alain J. Mbebi, Zoran Nikoloski
Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling…
Baptiste Py, Francesco Ciucci
The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression, which usually leverages the Euclidean norm. In this work, we show…
Authors not listed
Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…
Denice van Herwerden, Alexandros Nikolopoulos, Leon Barron, Jake O'Brien + 3 more
- 1. Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam - 2. MRC Centre for Environment and Health, Environmental Research Group, School of Public Health, Faculty of Medicine, Imperial College London, W12 0BZ, United Kingdom - 3. Queensland Alliance for Environmental Health Sciences…
Sterling G. Baird, Jeet N. Parikh, Taylor D. Sparks
Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that can be indistinguishable from the ground truth observation within…