The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we sugge...

We consider univariate regression estimation from an individual (non-random) sequence (x1, y1), (x2, y2), . . . ∈ IR×IR, which is stable in the sense that for each interval A ⊆ IR, (i) the limiting relative frequency of A under x1, x2, . . . is governed by an unknown probability distribution μ, and (ii) the limiting average of those yi with xi ∈ A is governed by an unknown regression function m...

The Perceptron algorithm elegantly solves binary classification problems that have a margin between positive and negative examples. Isotonic regression (fitting an arbitrary increasing function in one dimension) is also a natural problem with a simple solution. By combining the two, we get a new but very simple algorithm with strong guarantees. Our ISOTRON algorithm provably learns Single Index...

two forage species alfalfa (medicago sativa), and sainfoin (onobrychis viciifolia scop.), at two consecutive growing season (spring and summer), were studied for their chemical composition and in vitro gas production characteristics. data on cumulative gas production (ml gas/g dm) were fitted to the non-sigmoidal model, and for evaluation of model, the concordance correlation coefficient (ccc) ...

fluorescent monitoring of dna amplification is the basis of real-time pcr. absolute quantification can be achieved using a standard curve method. the standard curve is constructed by amplifying known amounts of standards under identical conditions to that of the samples.the objective of the current study is to propose a mathematical model to assess the acceptability of pcr resulys.four commerci...

background: existence or non-existence of adverse selection in insurance market is one of the important cases that have always been considered by insurers. adverse selection is one of the consequences of asymmetric information. theory of adverse selection states that high-risk individuals demand the insurance service more than low risk individuals do. methods: the presence of adverse selection ...

This paper aims at predicting businesses’ past due in service accounts as well as determining the variables that impact the likelihood of repayment. Two binary classification approaches, logistic regression and the decision tree, were conducted and compared. Both approaches have very good performances with respect to the accuracy. However, the decision tree only uses 10 predictors and reaches a...

Probabilistic models for binary spike patterns provide a powerful tool for understanding the statistical dependencies in large-scale neural recordings. Maximum entropy (or “maxent”) models, which seek to explain dependencies in terms of low-order interactions between neurons, have enjoyed remarkable success in modeling such patterns, particularly for small groups of neurons. However, these mode...