27 papers · ranked by Valyu relevance
Georg Hahn, Sharon M. Lutz, Nilanjana Laha, Christoph Lange
Penalized linear regression approaches that include an L_1_ term have become an important tool in statistical data analysis. One prominent example is the least absolute shrinkage and selection operator (Lasso), though the class of L_1_ penalized regression operators also includes the fused and graphical Lasso, the…
Islam M. Hammam, Amin K. El-Kharbotly, Yomna M. Sadek
Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly shorter life cycles driven by continuous product innovation. This study aims to develop a forecasting framework capable…
Xinyu Zhou, Pengtao Dang, Haixu Tang, Laura Xianlu Peng + 6 more
Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates…
Jong Woo Nam, Eun Young Choi, Jennifer A. Ailshire, Yao-yi Chiang
As environmental hazards become more frequent, it is critically important to understand their health impacts and identify individuals at disproportionately higher risk. Moderated Multiple Regression (MMR) provides a straightforward approach for investigating population heterogeneity by incorporating interaction terms…
Kumarjit Pathak, Jitin Kapila, Aasheesh Barvey, Nikit Gawande
In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target…
Georgia Tsiliki, Cristian R. Munteanu, Jose A. Seoane, Carlos Fernandez-Lozano + 2 more
'Carlos Fernandez-Lozano' 'Haralambos Sarimveis' 'Egon L. Willighagen'] Background Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect…
Sri Preethaa, Yuvaraj Natarajan, Arun Pandian Rathinakumar, Dong-Eun Lee + 3 more
'Dong-Eun Lee' 'Young Choi' 'Young-Jun Park' 'Chang-Yong Yi'] Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face…
Alma Andersson, Žaneta Andrusivová, Paulo Czarnewski, Xiaofei Li + 2 more
The increasing amount of spatial transcriptomics data prompts for means to amalgamate observations from distinct experiments, especially attractive is to cast quantities from different sources into a common coordinate frame-work (CCF) to relate signals across space. We here present a method that enables transfer of…
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…
Ashish Bhatia, Renato Cordeiro de Amorim, Vito De Feo
Regression analysis is employed to examine and quantify the relationships between input variables and a dependent and continuous output variable. It is widely used for predictive modelling in fields such as finance, healthcare, and engineering. However, traditional methods often struggle with real-world data…
Roméo Tayewo, François Septier, Ido Nevat, Gareth W. Peters + 1 more
'Donald J. Jacobs'] We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general…
Ayon Roy, Tausif Al Zubayer, Nafisa Tabassum, Muhammad Nazrul Islam + 1 more
'Abdus Sattar'] Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system…
Jonelle Angelo Cenita, Paul Richie Asuncion, Jayson Victoriano
**Special Issue on International Research Conference on Computer Engineering and Technology Education 2023 (IRCCETE 2023). Guest Associate Editors: Dr. Nelson C. Rodelas, PCpE (Computer Engineering Department, College of Engineering, University of the East-Caloocan City; nelson.rodelas@ue.edu.ph) and Engr. Ana…
Qiyao Wang, Haiyan Wang, Chetan Gupta, Aniruddha Rajendra Rao + 1 more
'Hamed Khorasgani'] Abstract—In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate time series regression problem.…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
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…
Julián Candia, John S. Tsang
Systems biology analysis often involves building predictive models by selecting informative features from a large number of measurements. The elastic net for generalized linear models is a popular regression and feature selection method, particularly useful when the number of features is greater than the sample size or…
Siruo Wang, Tyler H. McCormick, Jeffrey T. Leek
Many modern problems in medicine and public health leverage machine learning methods to predict outcomes based on observable covariates [1, 2, 3, 4]. In an increasingly wide array of settings, these predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between…
Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin
A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor…
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,”…
Syeda Sakira Hassan, Muhammad Farhan, Rahul Mangayil, Heikki Huttunen + 1 more
'Heikki Huttunen' 'Tommi Aho'] Background In bioprocess development, the needs of data analysis include (1) getting overview to existing data sets, (2) identifying primary control parameters, (3) determining a useful control direction, and (4) planning future experiments. In particular, the integration of multiple data…
Shovan Chowdhury, Yuxiao Lin, Bor Yann Liaw, Leslie Kerby
Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear…
Stamatia Zavitsanou, Zonghua Bo, Emanuele Casali, Matthew Langton + 1 more
Machine learning (ML) is currently transforming the field of chemistry by offering unparalleled efficiency in addressing complex challenges. Despite the progress made, a notable gap persists in the availability of user-friendly tools tailored to chemical problems involving small and sparse datasets. Here, we introduce…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
Mark de Rooij, Bunga Citra Pratiwi, Marjolein Fokkema, Elise Dusseldorp + 1 more
'Elise Dusseldorp' 'Henk Kelderman'] Machine and Statistical learning techniques become more and more important for the analysis of psychological data. Four core concepts of machine learning are the bias variance trade-off, cross-validation, regularization, and basis expansion. We present some early psychometric…
Authors not listed
This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and…
Bartłomiej Fliszkiewicz, Marcin Sajdak
The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical…