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...

method of least squares is a mathematical optimization technique that aims to determine the best fit function by minimizing the sum of the squares of the...

Least-squares spectral analysis, based on least squares fitting to known frequencies Lomb–Scargle periodogram, an approximation of the Least-squares spectral...

Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge...

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems...

mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed...

In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which observational...

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:...

in terms of the unknown parameters β0, β1, .... Therefore, for least squares analysis, the computational and inferential problems of polynomial regression...

Colors of noise Least-squares spectral analysis Noise spectral density Spectral density estimation Spectral efficiency Spectral leakage Spectral power distribution...

often assumed to be that which minimizes the sum of squared residuals. This is the ordinary least squares (OLS) approach. However, in cases where the dependent...

Lowater Least-squares spectral analysis List of spectroscopists Metamerism (color) Operando spectroscopy Scattering theory Spectral line ratios Spectral power...

computing the first few components in a principal component or partial least squares analysis. For very-high-dimensional datasets, such as those generated in...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

least-squares spectral analysis (LSSA) methods that use a least squares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most...

fluctuation analysis Digital signal processing Distributed lag Estimation theory Forecasting Frequency spectrum Hurst exponent Least-squares spectral analysis Monte...

mean square error (RMSE) is the square root of MSE. The sum of squares of errors (SSE) is the MSE multiplied by the sample size. Sum of squares of residuals...

Fourier series Fourier analysis for computing periodicity in evenly-spaced data Harmonic (mathematics) Least-squares spectral analysis for computing periodicity...

packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some...

notably wavelets and least-squares spectral analysis methods for unevenly spaced data. In signal processing, time–frequency analysis is a body of techniques...

calculation is based on the method of the least squares criterion. The goal is to minimize the sum of the squared vertical distances (residuals) between...

of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS...

with a single sinusoid is a special case of spectral density estimation and least-squares spectral analysis. A good starting value for C can be obtained...

Bounded variation Dynamic range compression Information theory Least-squares spectral analysis Non-local means Reverberation Sensitivity (electronics) Similarity...

Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting...

Powered partial least squares discriminant analysis Qualitative variation Randomization test for goodness of fit Relative risk Stratified analysis Tetrachoric...

Coefficient of variation Homoscedasticity Least-squares spectral analysis for computing a frequency spectrum with spectral magnitudes in % of variance or in dB...

A chi-squared test (also chi-square or χ2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are...

to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of...