The Regression Analysis of Binary Sequences

By D. R. Cox. 1958

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Citations in the corpus

Differentially-Private "Draw and Discard" Machine Learning (2018, background): “Logistic regression [10] and neural networks [45], introduced over half a century ago, follow a now-familiar paradigm of reading training data from the local disk and adjusting model weights until certain convergence criteria are met.”
Sequence-based determinants and prediction of fuzzy interactions in protein complexes. (2020, methodology): “To relate sequence features to different interaction modes, we used a binary logistic model [41], with two classes, disorder-to-order and disorder-to-disorder regions ( Methods , Fig.”
Machine Learning in Agriculture: A Review (2018, methodology): “Most known algorithms include linear regression and logistic regression [25], as well as stepwise regression [26].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Following this trend, two linear models for conducting classification were introduced: Maximum Entropy (MaxEnt) [215, 216] and logistic regression [105].”
When Do Neural Nets Outperform Boosted Trees on Tabular Data? (2023, methodology): “The methods include three GBDTs: CatBoost [38], LightGBM [31], and XGBoost [11]; 11 neural networks: DANet [10], FTTransformer [21], two MLPs [21], NODE [37], ResNet [21], SAINT [44], STG [51], TabNet [4], TabPFN [26], nad VIME [52]; and five baselines: Decision Tree [39], KNN [15], Logistic Regress”
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting (2022, methodology): “We used multinomial naive Bayes (John and Langley, 1995) and logistic regression (LR) (Cox, 1958) to classify text features represented by BOW and static word embeddings.”
Dynamics of Trade-By-Trade Price Movements: Decomposition and Models (2003, unknown): “This model structure was introduced by Cox (1958) (see Cox and Snell (1989) for an exposition) and has some significant advantages 1 .”
An Econometric Model of Network Formation With Degree Heterogeneity (2017, methodology): “Recently, in independent work, Charbonneau (2014) used sufficiency arguments to develop conditional estimators for nonlinear models with multiple fixed effects.”
Bounds on Parameters in Panel Dynamic Discrete Choice Models (2006, background): “(2) Recent empirical applications of this model include studies of labor force participation (Hyslop (1999)), health status (Contoyannis, Jones, and Rice (2002) and Halliday (2002)), product purchase behavior (Chintagunta, Kyriazidou, and Perktold (2001)) and welfare participation (Chay, Hoynes, and”
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning (2019, methodology): “This model is a simple approach to prediction which provides baseline accuracy scores for comparisons with other non-parametric machine learning models [17].”