Search · four archives

Results for regression framework

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

16 papers · ranked by Valyu relevance

01
PPubMed29 Aug 2018
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables
Tomislav Hengl, Madlene Nussbaum, Marvin N. Wright, Gerard B.M. Heuvelink + 2 more
Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe…
79relevance
02
PPubMed25 Aug 2017
Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models
Udo Boehm, Helen Steingroever, Eric-Jan Wagenmakers
An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific…
78relevance
03
PPubMed4 Nov 2025
Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability
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…
77relevance
04
PPubMed22 Dec 2025
Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network
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…
75relevance
05
PPubMed15 Sep 2015
RRegrs: an R package for computer-aided model selection with multiple regression models
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…
72relevance
06
PPubMed4 Jun 2025
Principal fitted component framework for robust support vector regression based on bounded loss: A simulation study with potential applications Principal fitted component framework for robust support vector regression
Aiman Tahir, Maryam Ilyas, Mohamed R. Abonazel
The inferential results regarding estimates of Support Vector Regression (SVR) are highly influenced by anomalies and ill-conditioned predictors. Excessive dimensions of data also make the model complex. To improve estimation accuracy, this paper introduces two modelling frameworks, Principal Component Robust Support…
70relevance
07
PPubMed27 Feb 2024
Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran
MohammadRasool Dehghani, Shahryar Jahani, Ali Ranjbar
Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior…
70relevance
08
PPubMed24 Jun 2025
On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching
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,”…
69relevance
09
PPubMed26 Sep 2022
A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction
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…
69relevance
10
PPubMed29 Aug 2023
Graph Regression Model for Spatial and Temporal Environmental Data—Case of Carbon Dioxide Emissions in the United States
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…
67relevance
11
PPubMed20 Jan 2016
Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting
Miltiadis Alamaniotis, Dimitrios Bargiotas, Lefteri H. Tsoukalas
Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting…
66relevance
12
PPubMed2 Feb 2024
Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models Three forest plot variations for visualizing linear models
Jonathan Fries, Sandra Oberleiter, Jakob Pietschnig, Tobias Otterbring
'Tobias Otterbring'] Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to…
63relevance
13
PPubMed7 Aug 2024
Forecasting CO 2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models
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…
62relevance
14
PPubMed26 Apr 2016
Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications
Guoqi Qian, Yuehua Wu, Davide Ferrari, Puxue Qiao + 1 more
'Frédéric Hollande'] Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective…
61relevance
15
PPubMed12 Aug 2013
Bioprocess data mining using regularized regression and random forests
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…
61relevance
16
PPubMed7 Feb 2019
Functional random forest with applications in dose-response predictions
Raziur Rahman, Saugato Rahman Dhruba, Souparno Ghosh, Ranadip Pal
Drug sensitivity prediction for individual tumors is a significant challenge in personalized medicine. Current modeling approaches consider prediction of a single metric of the drug response curve such as AUC or IC50. However, the single summary metric of a dose-response curve fails to provide the entire drug…
60relevance

Results for regression framework

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models

Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability

Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network

RRegrs: an R package for computer-aided model selection with multiple regression models

Principal fitted component framework for robust support vector regression based on bounded loss: A simulation study with potential applications Principal fitted component framework for robust support vector regression

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Graph Regression Model for Spatial and Temporal Environmental Data—Case of Carbon Dioxide Emissions in the United States

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting

Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models Three forest plot variations for visualizing linear models

Forecasting CO 2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Bioprocess data mining using regularized regression and random forests

Functional random forest with applications in dose-response predictions

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models

Adaptive demand forecasting framework with weighted ensemble of regression and machine learning models along life cycle variability

Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network

RRegrs: an R package for computer-aided model selection with multiple regression models

Principal fitted component framework for robust support vector regression based on bounded loss: A simulation study with potential applications Principal fitted component framework for robust support vector regression

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran

On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Graph Regression Model for Spatial and Temporal Environmental Data—Case of Carbon Dioxide Emissions in the United States

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting

Say farewell to bland regression reporting: Three forest plot variations for visualizing linear models Three forest plot variations for visualizing linear models

Forecasting CO 2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

Bioprocess data mining using regularized regression and random forests

Functional random forest with applications in dose-response predictions