In this work, we consider a multivariate regression model with one﹕ided errors. We assume for the regression function to lie in a general Hlder class and estimate it via a nonparametric local polynomial approac
Interactive ModelModel SelectionNonparametric RegressionSpline SmootherIn nonparametric multivariate regression analysis, we seek methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. The original additive models approximate a regression ...
Minimizing the Cost function (mean-square error) using SGD Algorithm Follow 1.0 (2) 474 Downloads Updated7 Jul 2020 View License Share Open in MATLAB Online Download Overview Functions Version History Reviews(2) Discussions(1) -Multivariate Regression using Stochastic Gradient Descent, Gradient Descent...
However, it is not LAD as α→ 0, it is square-root loss function. Now we introduce the Huber loss for the multivariate regression model (1),Hαn(B)=1n∑i=1nhα(yi−BTxi),wherehα(yi−BTxi)={12∥yi−BTxi∥22,∥yi−BTxi∥2≤α,α(∥yi−BTxi∥2−12α),∥yi...
摘要: Multivariate adaptive regression splines : regression analysis, linear model, dependent and independent variables, basis function, piecewise, ice hockey stick, recursive partitioning Frederic P. Miller, Agnes F. Vandome, John McBrewster, ed. Alphascript Publishing, 2010...
The trace of the scale matrix of the multivariate t-distribution is considered for estimation using a regression type estimator. The proposed estimator always dominates the usual maximum likelihood estimator under a quadratic loss function. The dominance behaviour is proved analytically as well as ...
Non-parametric regressionSensitivity indicesThe fact of estimating how a model output is influenced by the variations of inputs has become an important problematic in reliability and sensitivity analysis. This article is interested in estimating sensitivity indices useful to quantify the contribution of ...
regressionmultivariatemodelingComplex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, ...
function allows our newly adapted model to output a continuous outlier probability, whereas the architecture of Maciąg et al. (2021) only outputs a binary classification of outliers. However, when comparing our algorithm with other anomaly detection algorithms in Sect.5we clearly need to identify...
influence functionmultivariate regressionrobust estimationsscatter estimatorBased on the projection depth weighted mean and scatter estimation of the joint distribution of (x, y), we introduce a robust estimator of the regression coefficients for the multivariate linear model. The new estimator possesses ...