Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary ...(dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of ...regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.
Are American electric vehicle owners quitting? Dua, Rubal; Edwards, Alexander; Anand, Utkarsh ...
Transportation research. Part D, Transport and environment,
August 2024, 2024-08-00, Letnik:
133
Journal Article
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We investigate the proportion of U.S. plug-in electric vehicle (PEV) owners who discontinued PEV ownership by disposing their PEV and buying a non-PEV on their next purchase, termed PEV ...discontinuance. Analyzing 8457 new car buyers’ who disposed of their PEVs, we find ∼35 % discontinuance. Single-car moderate-income households owning a plug-in hybrid electric vehicle (PHEV) are likelier to discontinue than multi-car high-income households owning a battery electric vehicle (BEV). Former PEV owners, when purchasing a new vehicle, are unwilling to compromise on key issues such as electric driving range (minimum 330 miles for BEVs), charging station locations, recharge times, home charging unavailability, higher purchase prices, battery replacement costs, and lower resale values, which are also concerns shared by households that continue to own PEVs. These insights, drawn from PEV owner experiences rather than perceptions, can guide PEV policy designs and targeted leasing & brand loyalty programs to reduce discontinuance.
Evidence of microplastics has been found in a variety of marine ecosystems, terrestrial ecosystems, animal species, and sometimes even humans. There is a growing consensus that plastics and ...microplastics pose a threat to human health and food safety, both in terms of the environment and the food chain. Yet, there is currently no technically viable option for addressing the problem of microplastics in the environment. Understanding people's preferences is therefore critical for minimizing plastics and microplastics contamination. This study employed the ordered logistic regression model to examine the people’s readiness to restrict microplastic pollutions and its influencing elements, as well as their attitudes, perceptions and behaviors concerning plastics and microplastics. Face-to-face interviews were conducted at random, resulting in a total of 465 acceptable questionnaires. The findings expose a concerning lack of awareness about microplastics, as only 22% of respondents possessed prior knowledge, and a notable 66% remained uninformed about microplastics pollution. The desire to minimize microplastics emission was substantially influenced by a variety of factors, including family size, occupation, gender, familiarity with plastics and microplastics related to the health concerns and the environment. Gender differences become evident, with women exhibiting greater willingness than men to mitigate microplastics emission, and environmental practitioners displaying heightened motivation. Familiarity with plastics and microplastics particles enhanced the possibility that respondents would take action to reduce microplastics pollution. The study concludes by offering a number of policy reforms and legislative changes that would limit microplastics contamination during the plastic production and recycling processes.
•COVID-19.•SIR-model reducible to logistic regression.•Forecast uncertainty quantification.•Revealing effect of epidemic prevention measures.
Basing on existence of the mathematically sequential ...reduction of the three-compartmental (Susceptible-Infected-Recovered/Removed) model to the Verhulst (logistic) equation with the parameters determined by the basic characteristic of epidemic process, this model is tested in application to the recent data on COVID-19 outbreak reported by the European Centre for Disease Prevention and Control. It is shown that such a simple model adequately reproduces the epidemic dynamics not only qualitatively but for a number of countries quantitatively with a high degree of correlation that allows to use it for predictive estimations. In addition, some features of SIR model are discussed in the context, how its parameters and conditions reflect measures attempted for the disease growth prevention that is also clearly indicated by deviations from such model solutions.
Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general ...techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.
The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.
We conducted a Medline literature ...search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes.
We included 71 of 927 studies. The median sample size was 1,250 (range 72–3,994,872), with 19 predictors considered (range 5–563) and eight events per predictor (range 0.3–6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52–0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, −0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20–0.47) higher for ML.
We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) ...algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance.
While renewable energy technologies (RET) increase their share in power generation systems worldwide, some questions remain open, namely those concerning the opinion of the populations on new ...projects of these technologies. Given the long period of planning and large capital sums required by RET and, in some cases, the fact of being subsidized, it is desirable for decision-makers to acknowledge the public opinion and at least perceive if the opinions are rooted on biased perceptions. In this paper we propose a methodology for public perception and awareness assessment, involving an initial phase of data collection by means of a survey, followed by a phase of regression models construction resulting in predictive models of expected perceptions and attitudes towards RET. The models were translated in a free and easy to use computational Excel application and its usefulness was demonstrated for the case of four electricity RET in Portugal: hydro, wind, biomass and solar.
•Models for expected perception and attitudes towards renewable energy are proposed.•Ordered logistic and binary logistic regression models were used.•A survey for the Portuguese system was used to test the models implementation.•Models are demonstrated to be useful for hydro, wind, sun and biomass power.•Model results are useful for energy policy decision and investment decision making.
Previous research has established that higher levels of trait Honesty-Humility (HH) are associated with less dishonest behavior in cheating paradigms. However, only imprecise effect size estimates of ...this HH-cheating link are available. Moreover, evidence is inconclusive on whether other basic personality traits from the HEXACO or Big Five models are associated with unethical decision making and whether such effects have incremental validity beyond HH. We address these issues in a highly powered reanalysis of 16 studies assessing dishonest behavior in an incentivized, one-shot cheating paradigm (N = 5,002). For this purpose, we rely on a newly developed logistic regression approach for the analysis of nested data in cheating paradigms. We also test theoretically derived interactions of HH with other basic personality traits (i.e., Emotionality and Conscientiousness) and situational factors (i.e., the baseline probability of observing a favorable outcome) as well as the incremental validity of HH over demographic characteristics. The results show a medium to large effect of HH (odds ratio = 0.53), which was independent of other personality, situational, or demographic variables. Only one other trait (Big Five Agreeableness) was associated with unethical decision making, although it failed to show any incremental validity beyond HH.
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