Search for "Least squares" in article titles:

1. Least squares: We use the method of Least squares when we have a series of measures (xi, yi) with i = 1, 2, ..., n (i.e., we measured a set of values we called y, and each of these depended on the value ... [100%] 2023-12-14 [Mathematics]
2. Least squares: Least squares, also known as ordinary least squares analysis, is a method for linear regression that determines the values of unknown quantities in a statistical model by minimizing the sum of the squared residuals (the difference between the predicted and ... [100%] 2023-06-27
3. Least squares: The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a ... (Approximation method in statistics) [100%] 2024-11-01 [Least squares] [Single-equation methods (econometrics)]...
4. Least squares support vector machine: Least squares support vector machines (LS-SVM) are least squares versions of support vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis ... [63%] 2022-06-02 [Support vector machines] [Classification algorithms]...
5. Least squares inference in phylogeny: Least squares inference in phylogeny generates a phylogenetic tree based on an observed matrix of pairwise genetic distances and optionally a weight matrix. The goal is to find a tree which satisfies the distance constraints as best as possible. [63%] 2023-12-31 [Computational phylogenetics]
6. Least squares inference in phylogeny: Least squares inference in phylogeny generates a phylogenetic tree based on an observed matrix of pairwise genetic distances and optionally a weight matrix. The goal is to find a tree which satisfies the distance constraints as best as possible. (Generation of phylogenetic trees based on an observed matrix of pairwise genetic distances) [63%] 2024-10-16 [Computational phylogenetics]
7. Moving least squares: Moving least squares is a method of approximating a continuous functions from a set of eventually unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the approximation value ... [81%] 2023-06-16
8. Weighted least squares: Weighted least squares is a method of linear regression similar to the ordinary least squares method, except that points are weighted, which causes that some points have greater effect on the approximation than the others. The weighted least squares method ... [81%] 2023-06-11
9. Regularized least squares: Regularized 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. [81%] 2023-11-27 [Least squares] [Linear algebra]...
10. Linear least squares: Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. [81%] 2022-07-21 [Least squares] [Computational statistics]...
11. Generalized least squares: In statistics, generalized least squares (GLS) is a technique for estimating the unknown parameters in a linear regression model when there is a certain degree of correlation between the residuals in the regression model. In such cases, ordinary least squares ... (Statistical estimation technique) [81%] 2023-12-07 [Least squares] [Estimation methods]...
12. Least-Squares Method: A brief introduction to Least-Squares method, and its statistic meaning. This learning project offers learning activities and some application for Least-Squares Method. [81%] 2023-03-04 [Scientific computing] [Computer science]...
13. Polynomial least squares: In mathematical statistics, polynomial least squares comprises a broad range of statistical methods for estimating an underlying polynomial that describes observations. These methods include polynomial regression, curve fitting, linear regression, least squares, ordinary least squares, simple linear regression, linear least ... [81%] 2023-12-20 [Least squares]
14. Constrained least squares: In constrained least squares one solves a linear least squares problem with an additional constraint on the solution. This means, the unconstrained equation X β = y {\displaystyle \mathbf {X} {\boldsymbol {\beta }}=\mathbf {y} } must be fit as closely as possible (in ... [81%] 2023-03-13 [Least squares]
15. Regularized least squares: Regularized 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. [81%] 2023-10-08 [Least squares] [Linear algebra]...
16. Constrained least squares: In constrained least squares one solves a linear least squares problem with an additional constraint on the solution. This means, the unconstrained equation \displaystyle{ \mathbf {X} \boldsymbol {\beta} = \mathbf {y} }[/math] must be fit as closely as possible (in the ... [81%] 2023-06-24 [Least squares]
17. Least trimmed squares: Least trimmed squares (LTS), or least trimmed sum of squares, is a robust statistical method that fits a function to a set of data whilst not being unduly affected by the presence of outliers. It is one of a number ... [81%] 2023-06-25 [Robust statistics] [Robust regression]...
18. Total least squares: In 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 ... [81%] 2024-05-13 [Applied mathematics] [Least squares]...
19. Total least squares: In 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 ... (Statistical technique) [81%] 2024-09-17 [Applied mathematics] [Curve fitting]...
20. Moving least squares: Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested ... [81%] 2025-01-13 [Least squares]
21. Moving least squares: Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested ... [81%] 2025-03-06 [Least squares]
22. Weighted least squares: Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression. WLS is also a specialization ... (Method for model fitting in statistics) [81%] 2025-03-06 [Least squares]
23. Non-negative least squares: In mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed to become negative. That is, given a matrix A and a (column) vector of response ... [70%] 2022-06-28 [Least squares]
24. Least squares, method of: A method in the theory of errors (cf. Errors, theory of) for estimating unknown quantities on the basis of results of measurement involving random errors. (Mathematics) [70%] 2023-10-06
25. Iteratively reweighted least squares: The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm: by an iterative method in which each step involves solving a weighted least squares problem ... [70%] 2022-12-27 [Least squares]
26. Least-squares spectral analysis: Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum based on a least-squares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most used spectral method in science, generally boosts long ... (Periodicity computation method) [70%] 2023-12-13 [Algorithms] [Analysis of variance]...
27. Recursive least squares filter: Recursive least squares (RLS) is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in contrast to other algorithms such as the least ... [70%] 2023-03-01 [Digital signal processing] [Filter theory]...
28. Partial least squares regression: Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the ... (Statistical method) [70%] 2024-03-19 [Latent variable models] [Least squares]...
29. Least mean squares filter: Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the ... (Statistical algorithm) [70%] 2024-09-14 [Digital signal processing] [Filter theory]...
30. Least-squares support-vector machine: Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are ... [63%] 2023-10-11 [Support vector machines] [Classification algorithms]...
31. Proofs involving ordinary least squares: The purpose of this page is to provide supplementary materials for the ordinary least squares article, reducing the load of the main article with mathematics and improving its accessibility, while at the same time retaining the completeness of exposition. Define ... [63%] 2022-06-21 [Least squares]