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searching for Residual sum of squares 12 found (43 total)

Cochrane–Orcutt estimation (945 words) [view diff] exact match in snippet view article find links to article

procedure might converge to a local but not global minimum of the residual sum of squares. This problem disappears when using the Prais–Winsten transformation

Bayesian information criterion (1,671 words) [view diff] exact match in snippet view article find links to article

which is a biased estimator for the true variance. In terms of the residual sum of squares (RSS) the BIC is B I C = n ln ⁡ ( R S S / n ) + k ln ⁡ ( n )

Resampling (statistics) (2,178 words) [view diff] exact match in snippet view article

Without cross-validation, adding predictors always reduces the residual sum of squares (or possibly leaves it unchanged). In contrast, the cross-validated

Denise R. Osborn (2,504 words) [view diff] exact match in snippet view article find links to article

Modelling, 2018, vol. 74, 194-206. The asymptotic behaviour of the residual sum of squares in models with multiple break points (with Alastair R. Hall and

Multivariate adaptive regression spline (3,136 words) [view diff] no match in snippet view article find links to article

(1 − (effective number of parameters) / N)2) where RSS is the residual sum-of-squares measured on the training data and N is the number of observations

Ordinary least squares (8,935 words) [view diff] exact match in snippet view article find links to article

residuals (SSR) (also called the error sum of squares (ESS) or residual sum of squares (RSS)) is a measure of the overall model fit: S ( b ) = ∑ i = 1

Homoscedasticity and heteroscedasticity (3,191 words) [view diff] exact match in snippet view article find links to article

test requires that the squared residuals also be divided by the residual sum of squares divided by the sample size. Testing for groupwise heteroscedasticity

Multidimensional scaling (2,818 words) [view diff] exact match in snippet view article find links to article

Metric MDS minimizes the cost function called “stress” which is a residual sum of squares: Stress D ( x 1 , x 2 , . . . , x n ) = ∑ i ≠ j = 1 , . . . , n

Levenberg–Marquardt algorithm (3,211 words) [view diff] exact match in snippet view article find links to article

= λ 0 {\displaystyle \lambda =\lambda _{0}} and computing the residual sum of squares S ( β ) {\displaystyle S\left({\boldsymbol {\beta }}\right)} after

High-dimensional statistics (2,560 words) [view diff] exact match in snippet view article find links to article

minimises the maximum covariate-residual correlation, instead of the residual sum of squares as in the Lasso, subject to an ℓ 1 {\displaystyle \ell _{1}} constraint

Biostatistics (6,502 words) [view diff] exact match in snippet view article find links to article

consider an independent validation test set and the corresponding residual sum of squares (RSS) and R2 of the validation test set, not those of the training

Feature selection (6,933 words) [view diff] exact match in snippet view article find links to article

2004 SNPs Hill climbing Filter + Wrapper Naive Bayesian Predicted residual sum of squares Long 2007 SNPs Simulated annealing Naive bayesian Classification