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...measure of how ''h''_''i'' changes as a variable is added to the regression model. It is computed as: :''j'' = index of independent variable ...

...first=A. |year=1986 |chapter=On Assessing Prior Distributions and Bayesian Regression Analysis with g Prior Distributions |title=Bayesian Inference and Decision ...|first5=J. O. |year=2008 |title=Mixtures of g priors for Bayesian variable selection |journal=[[Journal of the American Statistical Association]] |volume=103 |i ...

{{About|the variable selection method|other uses|Counterfeit consumer goods}} ...nning for gold: model-X knockoffs for high dimensional controlled variable selection|journal=Journal of the Royal Statistical Society |series=Series B (methodol ...

{{Short description|Bayesian variable selection technique in statistics}} ...is Ishwaran & Rao (2005).<ref>{{Cite journal|title=Spike and slab variable selection: frequentist and Bayesian strategies|year=2005|doi=10.1214/0090536040000011 ...

{{Short description|Statistic in regression analysis}} ...=live }}</ref><ref>Allen, D. M. (1974), "The Relationship Between Variable Selection and Data Augmentation and a Method for Prediction," ''Technometrics'', 16, ...

...among a set of competitors for a given data set. Unlike most other [[model selection]] strategies, like the [[Akaike information criterion]] (AIC), the [[Bayesi ...hted performance measures, e.g. when searching for a [[regression analysis|regression]] model to perform particularly well in a portion of the [[covariate]] spac ...

...uous random variable#Continuous probability distribution|continuous random variable]] <math>X</math> is the function ...(statistics)|truncated]] [[normal distribution]]. If ''X'' is a [[random variable]] having a [[normal distribution]] with mean ''μ'' and variance ''σ''<sup>2 ...

{{Short description|statistical model selection}} ...the '''Constrained Minimum Criterion (CMC)''' is a criterion for selecting regression models founded on the classical theory of likelihood based inference. It is ...

...n <math>y \geq c</math>.<ref>{{cite book |first=Richard |last=Breen |title=Regression Models : Censored, Sample Selected, or Truncated Data |series=Quantitative ...riable ''X'' has ''F''(''x'') as its distribution function, the new random variable ''Y'' defined as having the distribution of ''X'' truncated to the semi-ope ...

{{short description|Statistical regression method}} ...s, the '''elastic net''' is a [[Regularization (mathematics)|regularized]] regression method that [[Linear combination|linearly combines]] the [[Taxicab geometry ...

...machine learning]] method designed to address the problem of [[best subset selection]]. It aims to determine which features or variables are crucial for optimal ...able in various statistical and machine learning tasks, including [[linear regression]], the [[Single-index model]], and other common predictive models.<ref name ...

...n of [[Classification algorithm|classification]] and [[Regression analysis|regression]] problems.{{sfn | Kogler | Traxler | 2017 | p=380}} Suppose that each random variable <math display="inline">X_i</math> falls within <math display="inline">[Y \p ...

...ce, where ''<math>{p}</math>'' is the number of independent variables in a regression model. This makes the fitted model likely to pass close to a high leverage Consider the [[linear regression]] model <math>{y}_i = \boldsymbol{x}_i^{\top}\boldsymbol{\beta}+{\varepsil ...

to find a vector <math>x</math> that is a stable solution to the regression problem. When the system is described by a matrix rather than a vector, thi ...iate regression]], and [[multi-task learning]]. Ideas of feature and group selection can also be extended to matrices, and these can be generalized to the nonpa ...

...gorithm selection aims to do. The only prerequisite for applying algorithm selection techniques is that there exists (or that there can be constructed) a set of ...1016/j.artint.2016.04.003|title = ASlib: A benchmark library for algorithm selection|journal = Artificial Intelligence|volume = 237|pages = 41–58|year = 2016|la ...

...[mutual information]] and [[Pearson's chi-squared test|chi-squared feature selection]]. Terms having very low frequency are not the best in representing the wh ...beling, the variable X is associated with membership in a cluster, and the variable Y is associated with the presence of a term.<ref>Manning, Christopher D., P ...

{{Short description|Non-parametric regression technique}} ...or = 2241837| citeseerx = 10.1.1.382.970 }}</ref> It is a [[non-parametric regression]] technique and can be seen as an extension of [[linear model]]s that autom ...

{{Short description|Type of regression analysis}} ...c Program Tree.png|thumb|[[Expression tree]] as it can be used in symbolic regression to represent a function.]] ...

...vector <math>\beta=(\beta_1, \dots, \beta_p) \in \mathbb{R}^{p}</math> of regression coefficients where <math>\beta</math> is often assumed to be [[Sparse matri ...and a [[response variable]] <math>y \in \mathbb{R}</math> is the [[linear regression|linear model]] ...

{{Regression bar}} ...Roger Joseph Boscovich]].<ref>{{cite book|chapter=Least Absolute Deviation Regression|title=The Concise Encyclopedia of Statistics|url=https://archive.org/detail ...