Logistic regression models.

By S. Domínguez-Almendros, N. Benítez-Parejo, A. González-Ramírez. 2011

696 citations recorded by the source corpus.

In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case. If the response variable is a qualitative variable (dichotomic or polychotomic), as for example the presence or absence of a disease, linear regression methodology is not applicable, and simple or multinomial logistic regression is used, as applicable.

Subfields: Statistics and Probability

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Citations in the corpus

Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables (2017, background): “…e.g., Jain et al., 1996) and natural neurons (see, e.g., Marreiros et al., 2008), cumulative distribution functions of usual probability density functions (see, e.g., Waissi and Rossin, 1996), logistic regression (see, e.g., Hilbe, 2009) and machine learning (see, e.g., Witten et al., 2016), etc.”
Driving factors of innovation in family and non-family SMEs (2018, background): “Because the coefficients of interactions in logit models are unintuitive to interpret, we follow Hoetker (2007), Hilbe (2009), and Mitchell (2012) and convey these results in a more meaningful way by providing graphical interpretations.”
A review of channel selection algorithms for EEG signal processing (2015, positive): “Fisher discriminant (FD) classifier [42–44].”
Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions (2012, methodology): “The most standard way to solve such a constrained regression problem is to use logistic regression [29,30] which uses logits of probabilities.”
Maximum Likelihood and Firth Logistic Regression of the Pedestrian Route Choice (2017, background): “Indeed, three empirical studies, each of which was conducted in Northampton, the United Kingdom (Verlander and Heydecker 1997); Beppu, Japan (Li and Tsukaguchi 2005); and Maastricht, the Netherlands (Borgers and Timmermans 1986), reported that walking distance is a single most important determinant ”
Read this paper if you want to learn logistic regression (2020, background): “For a more detailed treatment, see Hilbe (2009).”
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (2017, methodology): “The projection function means projecting Θ onto the orthant deﬁned by Ω.”