Article
Clinical Neurology
Dana Pisica, Ruben Dammers, Eric Boersma, Victor Volovici
Summary: This article provides a theoretical and practical tutorial on regression analysis, including data preparation, univariable and multivariable analysis, and model selection. It also demonstrates the application of regression analysis to real-world data and emphasizes the importance of multidisciplinary collaborations with professionals.
WORLD NEUROSURGERY
(2022)
Article
Computer Science, Information Systems
Marcin Czajkowski, Krzysztof Jurczuk, Marek Kretowski
Summary: By integrating the lasso estimator into the tree induction process, the interpretability of the decision tree can be controlled and its overall performance improved.
INFORMATION SCIENCES
(2023)
Article
Mathematical & Computational Biology
Yiran Zhang, Kellie J. Archer
Summary: In high-dimensional gene expression data, modeling for ordinal response helps to identify important genes for developing new diagnostic and prognostic tools for predicting or classifying stages of disease. A new Bayesian approach proposed in the study outperforms existing frequentist methods in simulation studies and is compared to frequentist methods in a study evaluating progression to hepatocellular carcinoma in hepatitis C infected patients.
STATISTICS IN MEDICINE
(2021)
Article
Automation & Control Systems
Karel Van Brantegem, Arno Strouwen, Peter Goos
Summary: In this article, the authors explore the use of locally and Bayesian D-and I-optimal experimental designs for the cumulative logit model. They also conduct a sensitivity study to understand the impact of model parameters and outcome categories on the optimal designs, using a polypropylene experiment as an example.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Didier Nibbering, Trevor J. Hastie
Summary: This study introduces a multinomial logistic regression model that penalizes the number of class-specific parameters, showing improved performance in both in-sample and out-of-sample situations compared to a standard model. The model clusters parameters by penalizing differences between class-specific parameter vectors, providing interpretable parameter estimates.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2022)
Article
Computer Science, Interdisciplinary Applications
Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon
Summary: The study proposed a new algorithm for ordinal regression that can be applied to model ordered or unordered categorical response data. This approach generalizes to a more flexible form and can shrink non-ordinal models towards their ordinal counterparts.
JOURNAL OF STATISTICAL SOFTWARE
(2021)
Article
Green & Sustainable Science & Technology
Dereje Fedasa Hordofa, Melekamu Ayele Badore
Summary: The study examined the impact of social, demographic, and economic factors on rural women's economic empowerment in Ethiopia's Dire Dawa region. It found that age, property ownership, education, and training had a negative effect, while social networks and media had a positive effect. The study recommended improving access to credit, land ownership, and training opportunities to promote gender equity and women's economic empowerment in agriculture.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Public, Environmental & Occupational Health
Molla Abate Ayele, Haile Mekonnen Fenta, Dereje Tesfaye Zike, Lijalem Melie Tesfaw
Summary: This study aimed to determine the factors of anemia levels among pregnant women in different zones in Ethiopia. The results showed a high prevalence of anemia in some regions of Ethiopia, and factors such as wealth index, age group, religion, and number of household members were found to be significant.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Public, Environmental & Occupational Health
Rong-Chang Jou, Ming-Che Chao
Summary: Objective motorcycles accounted for over 60% of motor vehicles in Taiwan. However, motorcycle crashes, especially among young riders, still occurred frequently. This study investigated the characteristics of novice motorcyclist crashes in Taiwan from January 2011 to December 2016. The study examined various risk factors, such as rider characteristics, licensing conditions, and the environment, that affect the severity of novice motorcyclist crashes. The results showed that factors such as underage or unlicensed riders, male sex, helmet use, drinking, college student status, frontal impact, urban or dry road, and daytime riding played significant roles in novice motorcyclist crashes. The study suggests that Taiwan should make policy adjustments and provide public education to address novice motorcycle crashes, as well as offer adequate driving training and a user-friendly environment for novice riders. Additionally, Taiwan should consider implementing graduated driver licensing systems to enhance the skills and supervision of new motorcyclists.
TRAFFIC INJURY PREVENTION
(2022)
Article
Mathematical & Computational Biology
Nadim Ballout, Cedric Garcia, Vivian Viallon
Summary: This study compared two regression analysis methods for disease subtypes in case-control studies, one based on data shared lasso and the other on L-1-norm penalized multi-nomial logistic regression. Experimental results showed that the non-symmetric formulation method is not recommended when homogeneity is high.
Article
Multidisciplinary Sciences
Yan Chen, Yulu Zhao
Summary: A novel penalty approach for the proportional hazards model under interval-censored failure time data structure is discussed, which approximates information criterion by smoothing the l(0) norm. This method eliminates the need for tedious hyperparameter tuning, improves efficiency of model fitting, and guarantees properties of continuity, sparsity, and unbiasedness for penalties. The proposed sparse estimation method shows high accuracy and efficiency in numerical results, and key factors affecting child mortality are identified using this method on Nigerian children data.
Article
Statistics & Probability
Jared S. Murray
Summary: The study introduces Bayesian additive regression trees (BART) for log-linear models such as multinomial logistic regression and count regression, addressing issues such as zero-inflation and overdispersion. New data augmentation strategies and prior distributions are developed to extend the application of BART beyond Gaussian data models, showcasing its utility with examples and a previously published dataset.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Mathematics, Applied
Jin-Jian Hsieh, Yun-Jhu Chen
Summary: This article discusses truncation data and the limitations of current analysis methods. It proposes the use of copulas to estimate the survival function of the interested event time and introduces two estimation procedures for the proportional hazard model and proportional odds model. The performance of these approaches is evaluated through simulation studies, and they are also applied to analyze real datasets.
Article
Health Care Sciences & Services
Sean M. Devlin, Glenn Heller
Summary: The manuscript introduces a new method for estimating concordance probability in time-to-event models, which requires input from analysts to determine separable survival regions for comparing risk scores between individuals. This method is analogous to clinically defined subgroups used for binary outcome area under the curve estimates.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Automation & Control Systems
Steven Siwei Ye, Oscar Hernan Madrid Padilla
Summary: Quantile regression is a statistical method for estimating conditional quantiles of a response variable, known for its robustness to outliers compared to l(2)-based methods. An adaptive quantile estimator using fused lasso penalty over a K-nearest neighbors graph achieves optimal rate of n(-1/d) with mild assumptions on data generation mechanism. Experimental results on simulated and real data show clear advantages over existing methods.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Theory & Methods
Moritz Berger, Gerhard Tutz, Matthias Schmid
STATISTICS AND COMPUTING
(2019)
Article
Statistics & Probability
Gerhard Tutz, Micha Schneider
JOURNAL OF APPLIED STATISTICS
(2019)
Article
Mathematics, Interdisciplinary Applications
Marie-Therese Puth, Gerhard Tutz, Nils Heim, Eva Muenster, Matthias Schmid, Moritz Berger
LIFETIME DATA ANALYSIS
(2020)
Article
Statistics & Probability
Gerhard Tutz
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
(2020)
Article
Statistics & Probability
Gerhard Tutz
Summary: Appropriate modelling of Likert-type items should consider the scale level and the specific role of the neutral middle category, and separate the neutral category to avoid biased estimates when modeling the effects of explanatory variables on the outcome. The proposed hierarchical model using binary response models as building blocks can be easily extended to include response style effects and non-linear smooth effects of explanatory variables.
INTERNATIONAL STATISTICAL REVIEW
(2021)
Article
Mathematics, Interdisciplinary Applications
Gerhard Tutz
JOURNAL OF MATHEMATICAL PSYCHOLOGY
(2020)
Article
Mathematics, Interdisciplinary Applications
Gerhard Tutz
Summary: The study introduces an improved method for ordinal trees that avoid the artificial assignment of scores and adopts the construction principle of binary models, combining trees and parametric models for prediction. The potential performance issues of random forests are also discussed, with proposals for ensemble models to achieve better predictive performance.
JOURNAL OF CLASSIFICATION
(2022)
Article
Mathematics, Interdisciplinary Applications
Gerhard Tutz
Summary: A comprehensive class of models is proposed for various types of responses, including continuous, binary, ordered categorical, and count type responses. These models are flexible and can accommodate a wide range of distributions.
Article
Statistics & Probability
Ingrid Mauerer, Gerhard Tutz
Summary: This study proposes a flexible and general heterogeneous multinomial logit model to study differences in choice behavior. The model captures heterogeneity that classical models cannot capture, indicates the strength of heterogeneity, and allows for examining the explanatory variables causing heterogeneity.
STATISTICAL METHODS AND APPLICATIONS
(2023)
Article
Social Sciences, Mathematical Methods
Gerhard Tutz
Summary: A new item response theory model for count data is proposed, which does not assume a fixed distribution for the responses and shows good performance in recovering parameters and response distributions, as well as flexibility in accommodating varying response distributions.
APPLIED PSYCHOLOGICAL MEASUREMENT
(2022)
Article
Education & Educational Research
Gerhard Tutz, Pascal Jordan
Summary: This article presents a general framework for latent trait item response models for continuous responses. Unlike classical test theory models, which differentiate between true scores and error scores, this model links the responses directly to latent traits. It is demonstrated that classical test theory models can be derived as special cases but the model class is much broader. The framework provides appropriate modeling for restricted responses, such as positive responses or responses within a certain interval. The model also extends common response time models and explores the role of the total score, leading to a modified total score. Various applications are illustrated, including one that considers covariates that may modify the response.
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS
(2023)
Review
Statistics & Probability
Gerhard Tutz
Summary: Ordinal models can be seen as being composed from simpler binary models, leading to a taxonomy of models; the structured overview covers existing models and shows how models can be extended to consider further effects of explanatory variables; particular attention is given to modeling additional heterogeneity and investigating the exact meaning of heterogeneity terms.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS
(2022)
Article
Economics
Gerhard Tutz, Moritz Berger
ECONOMETRICS AND STATISTICS
(2020)
Article
Social Sciences, Mathematical Methods
Gerhard Tutz, Gunther Schauberger
APPLIED PSYCHOLOGICAL MEASUREMENT
(2020)
Article
Computer Science, Interdisciplinary Applications
Gunther Schauberger, Gerhard Tutz
JOURNAL OF STATISTICAL SOFTWARE
(2019)