Regression with highly correlated predictors: variable omission is not the solution
Regression models have been in use for decades to explore and quantify the association
between a dependent response and several independent variables in environmental …
between a dependent response and several independent variables in environmental …
Eleven quick tips for data cleaning and feature engineering
Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …
many scientific studies, in any field, but alone might not be sufficient to generate robust and …
A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing …
Particulate matter (PM), one of the major air pollutants, is generated by variety of natural or
man-made sources, leading to acute and chronic diseases in humans since the last few …
man-made sources, leading to acute and chronic diseases in humans since the last few …
Identifying the determinants of regional raw milk prices in Russia using machine learning
S Kresova, S Hess - Agriculture, 2022 - mdpi.com
In this study, official data from Russia's regions for the period from 2015 to 2019 were
analysed on the basis of 12 predictor variables in order to explain the regional raw milk …
analysed on the basis of 12 predictor variables in order to explain the regional raw milk …
Intelligence and cortical morphometry: caveats in brain-behavior associations
It is well-established that brain size is associated with intelligence. But the relationship
between cortical morphometric measures and intelligence is unclear. Studies have …
between cortical morphometric measures and intelligence is unclear. Studies have …
Are machine learning algorithms more accurate in predicting vegetable and fruit consumption than traditional statistical models? An exploratory analysis
M Côté, MA Osseni, D Brassard, É Carbonneau… - Frontiers in …, 2022 - frontiersin.org
Machine learning (ML) algorithms may help better understand the complex interactions
among factors that influence dietary choices and behaviors. The aim of this study was to …
among factors that influence dietary choices and behaviors. The aim of this study was to …
Predicting the treatment outcomes of antidepressants using a deep neural network of deep learning in drug-naïve major depressive patients
Predicting the treatment response to antidepressants by pretreatment features would be
useful, as up to 70–90% of patients with major depressive disorder (MDD) do not respond to …
useful, as up to 70–90% of patients with major depressive disorder (MDD) do not respond to …
A critical discussion on developing molecular property prediction models: density of ionic liquids as example
Molecular property prediction models are powerful tools for screening or designing
chemicals to meet specific application requirements. However, many key aspects in model …
chemicals to meet specific application requirements. However, many key aspects in model …
The Importance of Random Effects in Variable Selection: A Case Study of Early Childhood Education
TM Pham - 2023 - dash.harvard.edu
Multilevel models are very common in statistical studies of education, as they account for the
nested structure of many data sets. This paper combines this methodology with variable …
nested structure of many data sets. This paper combines this methodology with variable …
[图书][B] Cognition and activity early after stroke
T Abzhandadze - 2021 - gupea.ub.gu.se
Introduction and aims: Cognitive impairment and dependency in activities of daily living
(ADL) are common consequences of stroke. Due to a decrease in the length of hospital stay …
(ADL) are common consequences of stroke. Due to a decrease in the length of hospital stay …