Abstract Purpose To construct a pain classification model using binary logistic regression to calculate probability and monitor based on heart rate variability (HRV) photoplethysmography (PPG) parameters. Methods Heat stimulation was used simulate for modeling the generation process, electrocardiography PPG signals were recorded simultaneously. After signal analysis, statistical analysis perfor...

Understanding the factors that influence voter turnout is a fundamentally important question in public policy and political science research. Bayesian logistic regression models are useful for incorporating individual level heterogeneity to answer these many other questions. When questions involve large data sets include demographic ethnic subgroups, however, standard Markov Chain Monte Carlo (...

We present in this paper some worst-case datasets of deterministic first-order methods for solving large-scale binary logistic regression problems. Under the assumption that number algorithm iterations is much smaller than problem dimension, with our it requires at least \begin{document}$ {{{\mathcal O}}}(1/\sqrt{\varepsilon}) $\end...

In a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. The use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. The simple and basic idea in this paper i...