interpolation (kriging) of the regression residuals. It is mathematically equivalent to the interpolation method variously called universal kriging and kriging with...

statistics, originally in geostatistics, kriging or Kriging (/ˈkriːɡɪŋ/), also known as Gaussian process regression, is a method of interpolation based on...

regression; a model with two or more explanatory variables is a multiple linear regression. This term is distinct from multivariate linear regression...

multivariate adaptive regression splines smoothing splines neural networks In Gaussian process regression, also known as Kriging, a Gaussian prior is assumed...

called regressors, predictors, covariates, explanatory variables or features). The most common form of regression analysis is linear regression, in which...

comprehensive Matlab toolbox for GP regression and classification STK: a Small (Matlab/Octave) Toolbox for Kriging and GP modeling Kriging module in UQLab framework...

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

(MPCA) Kalman filters Particle filters Gaussian process regression (kriging) Linear regression and extensions Independent component analysis (ICA) Principal...

Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...

combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models...

Regression diagnostic Regression dilution Regression discontinuity design Regression estimation Regression fallacy Regression-kriging Regression model validation...

distribution, with Kriging mean: E ⁡ ( X ∣ y ) = μ + K ( y − H μ ) , {\displaystyle \operatorname {E} (X\mid y)=\mu +K(y-H\mu ),} and Kriging covariance: cov...

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

distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead...

In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...

model to produce predictions or simulations is by regression-kriging (also known as universal kriging). With soil data, the model's deterministic component...

(GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the...

Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches"...

model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...

parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y...

regression methods, including regularized least squares (e.g., ridge regression), linear smoothers, smoothing splines, and semiparametric regression,...

arbitrariness In statistics, originally in geostatistics, Kriging or Gaussian process regression is a method of interpolation for which the interpolated...

statistics, which may use a terminology different from the one commonly used in kriging. The next section should clarify the mathematical/computational meaning...

which performs an auxiliary regression of the squared residuals on the independent variables. From this auxiliary regression, the explained sum of squares...

various applications in image signal processing. In a moving average regression model, a variable of interest is assumed to be a weighted moving average...