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23 papers · ranked by Valyu relevance

01
PPubMed14 Jan 2026
Modeling and inference of mixed dynamics and detection of causal emergent features
William Casey, Leigh Metcalf, Shirshendu Chatterjee, Heeralal Janwa + 3 more
Many real-world problems feature nonlinear dynamic processes. Classical mathematical models may be adequate to describe a single dynamic process in isolation, but can be easily undermined by two natural and simple kinds of phenomenological variations: the emergence (or activation) of an additional dynamic process, and…
92relevance
02
PPubMed29 Jun 2020
Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning
Joshua J. Levy, A. James O’Malley
Background Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due…
84relevance
03
PPubMed22 Nov 2007
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning
Emanuela Barbini, Gabriele Cevenini, Sabino Scolletta, Bonizella Biagioli + 2 more
'Bonizella Biagioli' 'Pierpaolo Giomarelli' 'Paolo Barbini'] Background Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of…
83relevance
04
AarXiv8 Aug 2020
Evidence of Predicting Early Signs of Corporate Bankruptcy Using Financial Ratios in the Indian Landscape
Adit Chopra, Abhi Bansal, Aryaman Wadhwa
Corporate Bankruptcy impacts the functioning of the economy adversely as it impacts the shareholders, financial lenders, operational lenders and government, thus making it pertinent to be able to predict when would a firm's performance deteriorate to a level where it has a high potential of filing for bankruptcy. This…
83relevance
05
PPubMed7 Oct 2016
Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data
Lihan Yan, Yongmin Sun, Michael R. Boivin, Paul O. Kwon + 1 more
This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of outliers, (b)…
82relevance
06
AarXiv2016
Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems
Bohdan M. Pavlyshenko
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition "Bosch Production Line Performance". We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed…
80relevance
07
BbioRxiv16 Dec 2019
Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…
80relevance
08
PPubMed7 Oct 2025
Analysing factors influencing railway accidents: A predictive approach using multinomial logistic regression and data mining Analysing factors influencing railway accidents
Jaroslav Mašek, Lucia Duricova, Juraj Čamaj, Hamed Aghaei
Railway accidents, particularly suicides and suicide attempts, significantly disrupt operations, cause delays in passenger and freight services, and result in varying degrees of infrastructure damage. This study focuses on identifying the relationship between suicide-related railway incidents, as the most frequent type…
79relevance
09
PPubMed2 Jul 2019
A weighted bootstrap approach to logistic regression modelling in identifying risk behaviours associated with sexual activity SAHARA-J: JOURNAL OF SOCIAL ASPECTS OF HIV/AIDS H. BRYDON ET AL.
Humphrey Brydon, Rénette Blignaut, Joachim Jacobs
The latest population estimates released by Statistics South Africa indicate that 25.03% of all deaths in 2017 in South Africa were AIDS-related. Along with these results, it is also reported that 7.06% of the population were living with HIV, with the HIV-prevalence among youth (aged 15-24) at 4.64% for 2017 (STATSSA.…
78relevance
10
AarXiv2022
Liu-type Shrinkage Estimators for Mixture of Logistic Regressions: An Osteoporosis Study
Elsayed Ghanem, Armin Hatefi, Hamid Usefi
The logistic regression model is one of the most powerful statistical methods for analysis of binary data. The logistic regression allows to use a set of covariates to explain the binary responses. The mixture of logistic regression models is used to fit heterogeneous populations through an unsupervised learning…
77relevance
11
BbioRxiv29 Jul 2022
A ready-to-use logistic Verhulst model implemented in R shiny to estimate growth parameters of microorganisms
Marc Garel, Lloyd Izard, Marthe Vienne, David Nerini + 2 more
In microbiology, the estimation of the growth rate of microorganisms is a critical parameter to describe a new strain or characterize optimal growth conditions. Traditionally, this parameter is estimated by selecting subjectively the exponential phase of the growth, and then determining the slope of this curve section…
77relevance
12
PPubMed17 Nov 2022
Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
Charalambos Gnardellis, Venetia Notara, Maria Papadakaki, Vasilis Gialamas + 2 more
'Vasilis Gialamas' 'Joannes Chliaoutakis' 'Ching-Wei Wang'] The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in…
77relevance
13
AarXiv2015
Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model
Toshiki Sato, Yuichi Takano, Ryuhei Miyashiro
This paper concerns a method of selecting a subset of features for a sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer quadratic optimization formulation for solving the problem based on a quadratic approximation of the logistic loss function. However, since there is a significant gap between…
76relevance
14
AarXiv2014
PLUTO: Penalized Unbiased Logistic Regression Trees
Wenwen Zhang, Wei‐Yin Loh
We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs…
75relevance
15
AarXiv2019
On Generalized q-logistic Distribution and its Characterizations
Seema S. Nair, Nicy Sebastian
Several generalizations of the logistic distribution, and certain related models, are proposed by many authors for modeling various random phenomena such as those encountered in data engineering, pattern recognition, and reliability assessment studies. A generalized q-logistic distribution is discussed here in the…
74relevance
16
BbioRxiv20 May 2019
A short review of machine learning methods for classifying the outcome of Gestational Diabetes
Shreeya Banerji
Diabetes mellitus is a growing problem, especially in developing countries. People suffering from diabetes have an increased risk of developing a number of serious health problems. Consistently high blood glucose levels can lead to serious diseases affecting the heart and blood vessels, eyes, kidney, etc. In addition…
74relevance
17
CchemRxiv24 May 2023
Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?
William J. Zamora Ramírez, Esteban Bertsch Aguilar, Sebastián Suñer Sánchez, Silvana Pinheiro
- 1. 1. CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, San José, Costa Rica - 2. 2. Laboratory of Computational Toxicology and Artificial Intelligence (LaToxCIA), Biological Testing Laboratory (LEBi), University of Costa Rica, San Pedro, San José, Costa Rica - 3. 3. Advanced Computing Lab…
72relevance
18
AarXiv3 Mar 2018
Asymmetric, closed-form, finite-parameter models of multinomial choice
Timothy Brathwaite, Joan L. Walker
Class imbalance, where there are great differences between the number of observations associated with particular discrete outcomes, is common within transportation and other fields. In the statistics literature, one explanation for class imbalance that has been hypothesized is an asymmetric (rather than the typically…
71relevance
19
BbioRxiv15 Jan 2016
Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy
Theo Knijnenburg, Gunnar Klau, Francesco Iorio, Mathew Garnett + 3 more
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding ‘actionable knowledge’ is becoming more important, but also more challenging as datasets grow in size and complexity. We…
70relevance
20
AarXiv2024
A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming
Vu Duc Anh, Tuan Anh Tran, Tran Ngoc Thang, Nguyễn Thị Ngọc Anh
Generalized linear regressions, such as logistic regressions or Poisson regressions, are long-studied regression analysis approaches, and their applications are widely employed in various classification problems. Our study considers a stochastic generalized linear regression model as a stochastic problem with chance…
68relevance
21
BbioRxiv23 Jun 2021
Parameter identifiability and model selection for sigmoid population growth models
Matthew J Simpson, Alexander P Browning, David J Warne, Oliver J Maclaren + 1 more
Sigmoid growth models, such as the logistic, Gompertz and Richards’ models, are widely used to study population dynamics ranging from microscopic populations of cancer cells, to continental-scale human populations. Fundamental questions about model selection and parameter estimation are critical if these models are to…
68relevance
22
CchemRxiv7 Jul 2023
Designing electrodes and electrolytes for batteries by leveraging deep learning
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
60relevance
23
CchemRxiv5 Jun 2023
Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?
Esteban Bertsch Aguilar, Sebastián Suñer Sánchez, Silvana Pinheiro, William J. Zamora Ramírez
- 1. 1. CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, San José, Costa Rica - 2. 2. Laboratory of Computational Toxicology and Artificial Intelligence (LaToxCIA), Biological Testing Laboratory (LEBi), University of Costa Rica, San Pedro, San José, Costa Rica - 3. 3. Advanced Computing Lab…
52relevance

Results for logistic model

Modeling and inference of mixed dynamics and detection of causal emergent features

Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

Evidence of Predicting Early Signs of Corporate Bankruptcy Using Financial Ratios in the Indian Landscape

Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data

Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems

Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

Analysing factors influencing railway accidents: A predictive approach using multinomial logistic regression and data mining Analysing factors influencing railway accidents

A weighted bootstrap approach to logistic regression modelling in identifying risk behaviours associated with sexual activity SAHARA-J: JOURNAL OF SOCIAL ASPECTS OF HIV/AIDS H. BRYDON ET AL.

Liu-type Shrinkage Estimators for Mixture of Logistic Regressions: An Osteoporosis Study

A ready-to-use logistic Verhulst model implemented in R shiny to estimate growth parameters of microorganisms

Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling

Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

PLUTO: Penalized Unbiased Logistic Regression Trees

On Generalized q-logistic Distribution and its Characterizations

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Asymmetric, closed-form, finite-parameter models of multinomial choice

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming

Parameter identifiability and model selection for sigmoid population growth models

Designing electrodes and electrolytes for batteries by leveraging deep learning

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Modeling and inference of mixed dynamics and detection of causal emergent features

Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

Evidence of Predicting Early Signs of Corporate Bankruptcy Using Financial Ratios in the Indian Landscape

Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data

Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems

Don’t Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning

Analysing factors influencing railway accidents: A predictive approach using multinomial logistic regression and data mining Analysing factors influencing railway accidents

A weighted bootstrap approach to logistic regression modelling in identifying risk behaviours associated with sexual activity SAHARA-J: JOURNAL OF SOCIAL ASPECTS OF HIV/AIDS H. BRYDON ET AL.

Liu-type Shrinkage Estimators for Mixture of Logistic Regressions: An Osteoporosis Study

A ready-to-use logistic Verhulst model implemented in R shiny to estimate growth parameters of microorganisms

Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling

Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model

PLUTO: Penalized Unbiased Logistic Regression Trees

On Generalized q-logistic Distribution and its Characterizations

A short review of machine learning methods for classifying the outcome of Gestational Diabetes

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Asymmetric, closed-form, finite-parameter models of multinomial choice

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming

Parameter identifiability and model selection for sigmoid population growth models

Designing electrodes and electrolytes for batteries by leveraging deep learning

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?