Background Using real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical onc
4.1 Regression methods In the regression analysis method, experimental data is used to establish the regression function between the independent variable and the dependent variable(s) in the research model, which is called the regression equation [65,66]. The application principle of a simple regress...
In the field of computational histopathology, regression tasks have not received extensive research compared to classification tasks. Typically, regression tasks are performed by regressing the probability of a pixel being the center of an object for detection or localization. However, detecting and local...
particularly in fields such as spintronics and quantum materials. Therefore, spin-resolved and angle-resolved photoemission spectroscopy (spin-resolved ARPES) has been at the forefront of research in these fields in recent years, thanks to its ability to provide ...
Multi-state models for the analysis of survival studies in biomedical research: an alternative to composite endpoints. In Proc. 13th International Joint Conference on Biomedical Engineering Systems and Technologies (eds De Maria, E. et al.) 194-199 (BIOSTEC, 2020); https://doi.org/10.5220/...
Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models Edited by Vittinghoff, E., Glidden, D. V., Shiboski, S... B Everitt - 《Statistical Methods in Medical Research》 被引量: 20发表: 2006年 3. Regression methods in biostatistics: linear, logistic, ...
Empirical research usually takes place in a space of available external information, like results from single studies, meta-analyses, official statistics o
Inference in RegressionDiscontinuity Designs, 7(1) R Journal, 38(2015)Social Research Center of ...
Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume...
Book 2018, Research Methods (Second Edition)Kerry Tanner Chapter Design of Experiments Regression discontinuity designs IES (2008) defines regression discontinuity designs as “designs in which participants are assigned to the intervention and the control conditions based on a cut-off score on a pre-...