23 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…
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…
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…
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…
Essoham Ali, Kim-Hung Pho
- 1. This study proposes some new models for family Zero-inflated Bernoulli models. - 2. This is the first work to be carried out for this model. - 3. Maximum likelihood estimator is used to check its performance of models under consideration. - 4. Many simulations and a real data set are performed in this research.
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.…
Seyyed Mahmood Ghasem, Johannes F. Fahrmann, Samir Hanash, Kim-Anh Do + 2 more
Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore…
John B. Carlin, Margarita Moreno‐Betancur
Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for which such modelling might be useful. More broadly, statistics is widely understood to provide a body of techniques for "modelling data"…
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…
Mark de Rooij, Ligaya Breemer, Dion Woestenburg, Frank Busing
We present a multidimensional data analysis framework for the analysis of ordinal response variables. Underlying the ordinal variables, we assume a continuous latent variable, leading to cumulative logit models. The framework includes unsupervised methods, when no predictor variables are available, and supervised…
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…
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment 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.…
Hansol X. Ryu, Manoj Srinivasan
Studying how humans perceive patterns in visually presented data is useful for understanding data-based decision-making and potentially understanding visually mediated sensorimotor control. We conducted experiments to examine how human subjects perform the simplest machine learning or statistical estimation tasks…
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)…
Luis Firinguetti, Manuel González-Navarrete, Romer Machaca-Aguilar
The beta regression model is a useful framework to model response variables that are rates or proportions, that is to say, response variables which are continuous and restricted to the interval (0,1). As with any other regression model, parameter estimates may be affected by collinearity or even perfect collinearity…
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.…
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…
Ping Li, Weijie Zhao
This report presents the open-source package https://github.com/pltrees/abcboost which implements the series of boosting works over the past many years (Li, 2008, 2009, 2010a,b; Li and Zhao, 2022a,c). In particular, this package includes mainly three lines of techniques, among which the following two techniques are…