In statistics, canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance...

Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical...

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization...

Regularized logistic regression is specifically intended to be used in this situation. Multicollinearity refers to unacceptably high correlations between...

West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences Archived 2024-10-04 at the Wayback Machine...

523–41. Julian C. Stanley, "II. Analysis of Variance," pp. 541–554. Lindley, D.V. (1987). "Regression and correlation analysis," New Palgrave: A Dictionary...

functions. In some contexts, a regularized version of the least squares solution may be preferable. Tikhonov regularization (or ridge regression) adds a...

distribution is said to be in canonical form (or natural form). Note that any distribution can be converted to canonical form by rewriting θ {\displaystyle...

example, a sample mean). The distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression...

feature representations, the impact of which is controlled by the robust regularized topological loss. Given the attacker's budget, they derived stability...

relationship. Notions of correlation for functional data include the following. FCCA is a direct extension of multivariate canonical correlation. For a pair of...

possible. In this case, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these...

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...

canonical correlation analysis (DCCA), introduced in 2013, employs neural networks to learn nonlinear transformations for maximizing the correlation between...

machines (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive...

contrast, standard regression will fail in these cases (unless it is regularized). Partial least squares was introduced by the Swedish statistician Herman...

continue to be used. Many non-standard regression methods, including regularized least squares (e.g., ridge regression), linear smoothers, smoothing splines...

statistical calibration problem Cancer cluster Candlestick chart Canonical analysis Canonical correlation Canopy clustering algorithm Cantor distribution Carpet...

transform Hartley transform Laplace transform Least-squares spectral analysis Linear canonical transform List of Fourier-related transforms Mellin transform...

regression models are generalized linear models with the logarithm as the (canonical) link function, and the Poisson distribution function as the assumed probability...

In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...

Generalized least squares Mixed model Elastic net regularization Ridge regression Lasso (statistics) Survival analysis Density estimation Kernel density estimation...

over the quantity one wants to estimate. MAP estimation is therefore a regularization of maximum likelihood estimation, so is not a well-defined statistic...

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...

seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship between one or more independent variables...

measurements. The Mixed model analysis allows measurements to be explicitly modeled in a wider variety of correlation and variance-covariance avoiding...

perturbation series of the free two-point function. In canonical quantization, the two-point correlation function can be written as:: 87  ⟨ Ω | T { ϕ ( x )...

; West, S. G.; Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (Report). Hosmer Jr, D. W., Lemeshow...

regression is also useful in that it can be used to select an optimally regularized cost function.) In most other regression procedures (e.g. logistic regression)...

pseudo-marginal Metropolis–Hastings algorithm. Rao–Blackwellized particle filter Regularized auxiliary particle filter Rejection-sampling based optimal particle filter...