Binary regression

In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output binary variable. Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in linear regression. Binary regression is usually analyzed as a special case of binomial regression, with a single outcome (), and one of the two alternatives considered as "success" and coded as 1: the value is the count of successes in 1 trial, either 0 or 1. The most common binary regression models are the logit model (logistic regression) and the probit model (probit regression). Binary regression is principally applied either for prediction (binary classification), or for estimating the association between the explanatory variables and the output. In economics, binary regressions are used to model binary choice. Binary regression models can be interpreted as latent variable models, together with a measurement model; or as probabilistic models, directly modeling the probability. The latent variable interpretation has traditionally been used in bioassay, yielding the probit model, where normal variance and a cutoff are assumed. The latent variable interpretation is also used in item response theory (IRT). Formally, the latent variable interpretation posits that the outcome y is related to a vector of explanatory variables x by where and , β is a vector of parameters and G is a probability distribution. This model can be applied in many economic contexts. For instance, the outcome can be the decision of a manager whether invest to a program, is the expected net discounted cash flow and x is a vector of variables which can affect the cash flow of this program. Then the manager will invest only when she expects the net discounted cash flow to be positive. Often, the error term is assumed to follow a normal distribution conditional on the explanatory variables x. This generates the standard probit model.

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (64)

Personnes associées (19)

Unités associées (10)

Concepts associés (3)

Cours associés (18)

Séances de cours associées (53)

MOOCs associés (2)

Données de comptage

In statistics, count data is a statistical data type describing countable quantities, data which can take only the counting numbers, non-negative integer values {0, 1, 2, 3, ...}, and where these integers arise from counting rather than ranking. The statistical treatment of count data is distinct from that of binary data, in which the observations can take only two values, usually represented by 0 and 1, and from ordinal data, which may also consist of integers but where the individual values fall on an arbitrary scale and only the relative ranking is important.

Binary data

Binary data is data whose unit can take on only two possible states. These are often labelled as 0 and 1 in accordance with the binary numeral system and Boolean algebra. Binary data occurs in many different technical and scientific fields, where it can be called by different names including bit (binary digit) in computer science, truth value in mathematical logic and related domains and binary variable in statistics. A discrete variable that can take only one state contains zero information, and is the next natural number after 1.

Régression logistique

En statistiques, la régression logistique ou modèle logit est un modèle de régression binomiale. Comme pour tous les modèles de régression binomiale, il s'agit d'expliquer au mieux une variable binaire (la présence ou l'absence d'une caractéristique donnée) par des observations réelles nombreuses, grâce à un modèle mathématique. En d'autres termes d'associer une variable aléatoire de Bernoulli (génériquement notée ) à un vecteur de variables aléatoires . La régression logistique constitue un cas particulier de modèle linéaire généralisé.

Impact de la covariable binaire: 2x2 Tableaux de contingence MATH-413: Statistics for data science

Explore l'impact d'une covariable binaire sur les réponses binaires à l'aide de tableaux 2x2.

Régression linéaire généralisée : classification BIO-322: Introduction to machine learning for bioengineers

Explorer la régression linéaire généralisée, la classification, les matrices de confusion, les courbes ROC et le bruit dans les données.

Géométrie et moindres carrés MATH-341: Linear models

Discute de la géométrie des moindres carrés, en explorant les perspectives des lignes et des colonnes, les hyperplans, les projections, les résidus et les vecteurs uniques.

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Zhixiong Jin

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

Basel2023

Multimodal characterization of murine gastruloid development

Matthias Lütolf, Alexandre Gauthier Aurèle Mayran, Stefano Davide Vianello, Raphaël Ortiz

Gastruloids are 3D structures generated from pluripotent stem cells recapitulating fundamental principles of embryonic pattern formation. Using single-cell genomic analysis, we provide a resource mapping cell states and types during gastruloid development ...

CELL PRESS2023

Creative approaches to both planned and unplanned new practices remain an essential, yet somewhat invisible, part of sustaining living heritage. This collection of essays by interdisciplinary scholars and practitioners draws attention to how decisions are ...

Lettera Ventidue2023

Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t

Discrete choice models allow for the analysis and prediction of individuals' choice behavior. The objective of the course is to introduce both methodological and applied aspects, in the field of marke

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications.