25 papers · ranked by Valyu relevance
Natchalee Srimaneekarn, Anthony Hayter, Wei Liu, Chanita Tantipoj
Multivariate analysis with binary response is extensively utilized in dental research due to variations in dichotomous outcomes. One of the analyses for binary response variable is binary logistic regression, which explores the associated factors and predicts the response probability of the binary variable. This…
Henry R. Scharf, Xinyi Lu, Perry J. Williams, Mevin B. Hooten
> Abstract. Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability surface that gives rise to the binary observations and novel methodology is required. This has generated a substantial literature…
Afiqah Saffa Suriaslan, I Nyoman Budiantara, Vita Ratnasari
In recent years, Truncated Spline estimators in nonparametric regression for quantitative data have gained significant attention. However, in practical applications, it is common to encounter situations where the response variable is qualitative (binary). As a result, Truncated Spline nonparametric regression models…
Sheng‐Mao Chang
Screening before model building is a reasonable strategy to reduce the dimension of regression problems. Sure independence screening is an efficient approach to this purpose. It applies the slope estimate of a simple linear regression as a surrogate measure of the association between the response and the predictor so…
Lü Tian, Ash A. Alizadeh, Andrew J. Gentles, Robert Tibshirani
We consider a setting in which we have a treatment and a large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way…
Vita Ratnasari, Purhadi, Marisa Rifada, Andrea Tri Rian Dani
Logit regression (or logistic regression) is a statistical analysis of categorical data. The binary responses have two categories. We present the Bivariate Polynomial Binary Logit Regression (BPBLR), which extends logit regression by modeling two correlated binary response variables. This model uses a polynomial…
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…
Condori Condori Nelyda Ayde, Mamani Mamani Ilma Magda, Cruz Paredes Soledad Epifania, Torres-Cruz Fred
'Cruz Paredes Soledad Epifania' 'Torres-Cruz Fred'] Abstract—Cancer is a tumor that affects people worldwide, with a higher incidence in females but not excluding males. It ranks among the top five deadliest types of cancer, particularly prevalent in less developed countries with deficient healthcare programs. Finding…
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)…
Rossana O. Souza, Wellington Francisco Rodrigues, Bráulio R. G. M. Couto, Marcos A. dos Santos
Logistic regression remains a widely used classification method due to its interpretability and computational efficiency, but its direct application to high-dimensional biomedical data is limited when the number of features greatly exceeds the number of samples. In this paper, we propose a reformulated logistic…
Maryam Sadiq, Nasser A. Alsadhan, Ramla Shah, Sidra Younas + 2 more
'Zahid Rasheed' 'Suyan Tian'] Variable selection methods are very popular, especially in the field of big data with large predictors. These procedures improve the accuracy and performance of the model by eliminating irrelevant and redundant variables. The main contribution of this study is to couple a logit model with…
Mark de Rooij, Patrick J. F. Groenen
Logistic regression is a commonly used method for binary classification. Researchers often have more than a single binary response variable and simultaneous analysis is beneficial because it provides insight into the dependencies among response variables as well as between the predictor variables and the responses.…
Kyungmin Lim, Su-Young Kim
In the structural equation modeling framework, binary variable models are generally considered a special case of ordinal variable models, as both involve similar scale assignment processes. However, the scaling processes of the two model types differ, with these differences becoming increasingly pronounced in the…
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…
Satwik Acharyya, Debdeep Pati, Dipankar Bandyopadhyay, Shumei Sun
Beta distributions are commonly used to model proportion valued response variables, commonly encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a…
Jordan Dotson, Lucy van Dijk, Jacob Timmerman, Samantha Grosslight + 5 more
Optimization of catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantio-, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands.…
Michal Ozery-Flato, Tal El-Hay, Ranit Aharonov, Naama Parush-Shear-Yashuv + 6 more
Electronic health records and health insurance claims, providing observational data on millions of patients, offer great opportunities, and challenges, for population health studies. The objective of this study is to utilize observational data for identifying subpopulations that are likely to benefit from a given…
Yuval Ben Dror
Over recent decades, extensive research has aimed to overcome the restrictive underlying assumptions required for a Generalized Linear Model to generate accurate and meaningful predictions. These efforts include regularizing coefficients, selecting features, and clustering ordinal categories, among other approaches.…
Lance F. Merrick, Dennis N. Lozada, Xianming Chen, Arron H. Carter
Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT)…
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…
Authors not listed
Phase equilibrium calculations are crucial in chemical engineering design and optimization processes. The PC-SAFT equation of state (EoS) can precisely calculate phase equilibrium, but is relatively complex and computationally intensive. Surrogate models are mathematically simple models that map or regress the…
Alexander Pomberger, Nicholas Jose, David Walz, Jens Meissner + 4 more
Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base…
А. Н. Вараксин, Vladimir G. Panov
In this paper, we consider the problem of treating linear regression equation coefficients in the case of correlated predictors. It is shown that in general there are no natural ways of interpreting these coefficients similar to the case of single predictor. Nevertheless we suggest linear transformations of predictors…
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…
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…