title: Empirical Bayes logistic regression.
abstract: We construct a diagnostic predictor for patient disease status based on a single data set of mass spectra of serum samples together with the binary case-control response. The model is logistic regression with Bernoulli log-likelihood augmented either by quadratic ridge or absolute L1 penalties. For ridge penalization using the singular value decomposition we reduce the number of variables for maximization to the rank of the design matrix. With log-likelihood loss, 10-fold cross-validatory choice is employed to specify the penalization hyperparameter. Predictive ability is judged on a set-aside subset of the data.
authors.affiliation: University of Kent. fs54@kent.ac.uk
authors.forename: Foteini
authors.initials: F
authors.lastname: Strimenopoulou
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000097:blood / Q000175:diagnosis; D016022:Case-Control Studies; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D013058:Mass Spectrometry / Q000706:statistics & numerical data; D009363:Neoplasm Proteins / Q000097:blood; D040901:Proteomics / Q000706:statistics & numerical data
chemical_list: D009363:Neoplasm Proteins