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weighted linear matrix

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  • Linear least squares — is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to measurements obtained from experiments. The goals of linear least squares are to extract predictions from the… …   Wikipedia

  • Linear least squares/Proposed — Linear least squares is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to observations obtained from experiments. Mathematically, it can be stated as the problem of… …   Wikipedia

  • Linear least squares (mathematics) — This article is about the mathematics that underlie curve fitting using linear least squares. For statistical regression analysis using least squares, see linear regression. For linear regression on a single variable, see simple linear regression …   Wikipedia

  • Linear regression — Example of simple linear regression, which has one independent variable In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X. The case of one… …   Wikipedia

  • Linear model — In statistics the linear model is given by:Y = X eta + varepsilonwhere Y is an n times;1 column vector of random variables, X is an n times; p matrix of known (i.e. observable and non random) quantities, whose rows correspond to statistical… …   Wikipedia

  • Weighted mean — The weighted mean is similar to an arithmetic mean (the most common type of average), where instead of each of the data points contributing equally to the final average, some data points contribute more than others. The notion of weighted mean… …   Wikipedia

  • Iteratively re-weighted least squares — The method of iteratively re weighted least squares (IRLS) is a numerical algorithm for minimizing any specified objective function using a standard weighted least squares method such as Gaussian elimination. Whereas techniques are widely… …   Wikipedia

  • Non-linear least squares — is the form of least squares analysis which is used to fit a set of m observations with a model that is non linear in n unknown parameters (m > n). It is used in some forms of non linear regression. The basis of the method is to… …   Wikipedia

  • Generalized linear array model — In statistics, the generalized linear array model(GLAM) is used for analyzing the data sets with array structure. It based on the generalized linear model with the regression matrix written as a Kronecker product. Overview In the article… …   Wikipedia

  • Hat matrix — In statistics, the hat matrix, H, maps the vector of observed values to the vector of fitted values. It describes the influence each observed value has on each fitted value.[1] The diagonal elements of the hat matrix are the leverages, which… …   Wikipedia

  • Non-negative matrix factorization — NMF redirects here. For the bridge convention, see new minor forcing. Non negative matrix factorization (NMF) is a group of algorithms in multivariate analysis and linear algebra where a matrix, , is factorized into (usually) two matrices, and… …   Wikipedia

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