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Targeting: Logistic Regression, Special Cases and Extensions
ISPRS International Journal of Geo-Information, 2014 Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form ...
Helmut Schaeben
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Multinomial latent logistic regression [PDF]
, 2016 University of Technology Sydney. Faculty of Engineering and Information Technology.We are arriving at the era of big data. The booming of data gives birth to more complicated research objectives, for which it is important to utilize the superior ...
Xu, Zhe
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CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model
Nucleic Acids Research, 2013 Thousands of novel transcripts have been identified using deep transcriptome sequencing. This discovery of large and ‘hidden’ transcriptome rejuvenates the demand for methods that can rapidly distinguish between coding and noncoding RNA. Here, we present
Liguo Wang +5 more
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Common pitfalls in statistical analysis: Logistic regression
Perspectives in Clinical Research, 2017 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).
Priya Ranganathan +2 more
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Semi-Parallel logistic regression for GWAS on encrypted data
BMC Medical Genomics, 2020 The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants ...
Miran Kim +3 more
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Robust logistic regression for insurance risk classification [PDF]
, 2001 Risk classification is an important part of the actuarial process in Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making.
Flores, Esteban, Garrido, José
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On Coresets for Logistic Regression
, 2018 Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce
Munteanu A. +3 more
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, 2020 Diabetes is one of the global concerns in the healthcare domain and one of the leading challenges locally in Saudi Arabia. The prevalence of diabetes is anticipated to rise; early prediction of individuals at high risk of diabetes is a significant ...
Tahani Daghistani, Riyad Alshammari
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A proof of convergence of multi-class logistic regression network [PDF]
, 1941 This paper revisits the special type of a neural network known under two names. In the statistics and machine learning community it is known as a multi-class logistic regression neural network.
Rychlik, Marek
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Bridging logistic and OLS regression [PDF]
There is broad consensus that logistic regression is superior to ordinary least squares (OLS) regression at predicting the probability of an event. OLS is still widely used in binary choice models because its coefficients are easier to interpret, while ...
Kapsalis, Constantine
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