Loi inverse-χ²En théorie des probabilités et en statistique, la loi inverse- (ou loi du inverse) est la loi de probabilité de la variable aléatoire dont l'inverse suit une loi du χ2. Une variante par changement d'échelle existe également. Cette loi est utilisée en inférence statistique. Si X suit une loi inverse-χ, on notera : . Si X suit une loi du χ2 à degrés de liberté, alors est de loi inverse-χ à degrés de liberté. Sa densité de probabilité est donnée par : où est la fonction gamma et est appelé le nombre de degrés de liberté.
Test unitaireEn programmation informatique, le test unitaire (ou « T.U. », ou « U.T. » en anglais) est une procédure permettant de vérifier le bon fonctionnement d'une partie précise d'un logiciel ou d'une portion d'un programme (appelée « unité » ou « module »). Dans les applications non critiques, l'écriture des tests unitaires a longtemps été considérée comme une tâche secondaire. Cependant, les méthodes Extreme programming (XP) ou Test Driven Development (TDD) ont remis les tests unitaires, appelés « tests du programmeur », au centre de l'activité de programmation.
Leverage (statistics)In statistics and in particular in regression analysis, leverage is a measure of how far away the independent variable values of an observation are from those of the other observations. High-leverage points, if any, are outliers with respect to the independent variables. That is, high-leverage points have no neighboring points in space, where is the number of independent variables in a regression model. This makes the fitted model likely to pass close to a high leverage observation.
Robust regressionIn robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables and a dependent variable. Standard types of regression, such as ordinary least squares, have favourable properties if their underlying assumptions are true, but can give misleading results otherwise (i.e. are not robust to assumption violations).
Generalized chi-squared distributionIn probability theory and statistics, the generalized chi-squared distribution (or generalized chi-square distribution) is the distribution of a quadratic form of a multinormal variable (normal vector), or a linear combination of different normal variables and squares of normal variables. Equivalently, it is also a linear sum of independent noncentral chi-square variables and a normal variable. There are several other such generalizations for which the same term is sometimes used; some of them are special cases of the family discussed here, for example the gamma distribution.
Matrice involutiveIn mathematics, an involutory matrix is a square matrix that is its own inverse. That is, multiplication by the matrix A is an involution if and only if A2 = I, where I is the n × n identity matrix. Involutory matrices are all square roots of the identity matrix. This is simply a consequence of the fact that any invertible matrix multiplied by its inverse is the identity. The 2 × 2 real matrix is involutory provided that The Pauli matrices in M(2, C) are involutory: One of the three classes of elementary matrix is involutory, namely the row-interchange elementary matrix.
Simple linear regressionIn statistics, simple linear regression is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. The adjective simple refers to the fact that the outcome variable is related to a single predictor.
Test (informatique)vignette|Une programmeuse écrivant du code Java avec JUnit. En informatique, un test désigne une procédure de vérification partielle d'un système. Son objectif principal est d'identifier un nombre maximal de comportements problématiques du logiciel. Il permet ainsi, dès lors que les problèmes identifiés seront corrigés, d'en augmenter la qualité. D'une manière plus générale, le test désigne toutes les activités qui consistent à rechercher des informations quant à la qualité du système afin de permettre la prise de décisions.
Test de validationUn test de validation est un type de test informatique qui permet de vérifier si toutes les exigences client, décrites dans le document de spécification du logiciel, sont respectées. Les tests de validation se décomposent généralement en plusieurs phases : Validation fonctionnelle : les tests fonctionnels assurent que les différents modules ou composants implémentent correctement les exigences client. Ces tests peuvent être de type valide, invalide, inopportuns, etc.
Bayesian multivariate linear regressionIn statistics, Bayesian multivariate linear regression is a Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random variables rather than a single scalar random variable. A more general treatment of this approach can be found in the article MMSE estimator. Consider a regression problem where the dependent variable to be predicted is not a single real-valued scalar but an m-length vector of correlated real numbers.
Total least squaresIn applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account. It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix.
Regularized least squaresRegularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions.
