Tootfinder

Semi-parametric Functional Classification via Path Signatures Logistic Regression
Pengcheng Zeng, Siyuan Jiang
https://arxiv.org/abs/2507.06637 https://

Semi-parametric Functional Classification via Path Signatures Logistic Regression
We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates. Classical functional logistic regression models rely on linear assumptions and fixed basis expansions, which limit flexibility and degrade performance under irregular sampling. PSLR overcomes these issues by leveraging truncated path signatures to construct a finite-dimensional, basis-free representation that captures nonlinear and cross-channe…

Predicting Post-Surgical Complications with Quantum Neural Networks: A Clinical Study on Anastomotic Leak
Vojt\v{e}ch Nov\'ak, Ivan Zelinka, Lenka P\v{r}ibylov\'a, Lubom\'ir Mart\'inek, Vladim\'ir Ben\v{c}urik
https://arxiv.org/abs/2506.01708

Predicting Post-Surgical Complications with Quantum Neural Networks: A Clinical Study on Anastomotic Leak
Quantum computing represents a transformative leap in computational power, with the potential to revolutionize various fields, including healthcare. In this study, we explore the application of quantum machine learning in medical diagnostics, specifically focusing on the prediction of anastomotic leak, a severe post-surgical complication. By comparing quantum neural networks implemented with different ansatz functions to classical classification approaches-including logistic regression, support…

What does making money have to do with crime?: A dive into the National Crime Victimization survey
Sydney Anuyah
https://arxiv.org/abs/2506.04240 https://

What does making money have to do with crime?: A dive into the National Crime Victimization survey
In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital stat…

General measures of effect size to calculate power and sample size for Wald tests with generalized linear models
Amy L Cochran, Shijie Yuan, Paul J Rathouz
https://arxiv.org/abs/2506.22324

General measures of effect size to calculate power and sample size for Wald tests with generalized linear models
Power and sample size calculations for Wald tests in generalized linear models (GLMs) are often limited to specific cases like logistic regression. More general methods typically require detailed study parameters that are difficult to obtain during planning. We introduce two new effect size measures for estimating power, sample size, or the minimally detectable effect size in studies using Wald tests across any GLM. These measures accommodate any number of predictors or adjusters and require on…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/2]:
- Robust estimation of parameters in logistic regression via solving the Cramer-von Mises type L2 o...
Jiwoong Kim

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Dana Lupsa, Sanda-Maria Avram
https://arxiv.org/abs/2506.15650 https://

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect clas…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
Jonas Lau, Annie Tran
https://arxiv.org/abs/2506.15294 h…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt …

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
Tim Ortkamp, Habiba Sallem, Semi Harrabi, Martin Frank, Oliver J\"akel, Julia Bauer, Niklas Wahl
https://arxiv.org/abs/2506.13539

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
In proton therapy of low-grade glioma (LGG) patients, contrast-enhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced lesions. From the observation that CEBLs tend to concentrate in regions of increased dose-averaged linear energy transfer (LET) and proximal to the ventricular system, the probability of lesion origin (POLO) model has been established as a multivariate logistic regression model for the voxel-wise probability prediction o…

Bayesian Hybrid Machine Learning of Gallstone Risk
Chitradipa Chakraborty, Nayana Mukherjee
https://arxiv.org/abs/2506.14561 https://…

Bayesian Hybrid Machine Learning of Gallstone Risk
Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effective clinical management. Although logistic regression remains a standard tool for assessing associations between predictors and gallstone status, it often underperforms in high-dimensional settings and may fail to capture intricate relationships among variables. To address these …