In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust reg...

Objectives: We introduce a new method for reducing crime in hot spots and across cities through ridge estimation. In doing so, our goal is to explore the application of density ridges patrol optimization, contribute policing literature police patrolling reduction strategies. Methods: make use subspace-constrained mean shift algorithm, recently introduced approach estimation further developed co...

We built 3-D and 1-D look up tables (LUTs) to transform a user’s desired device-independent colors (CIELab) to the device-dependent color space (RGB). We considered experimental adaptive neighborhood and estimation methods for building the 3-D and 1-D LUTs. Methods of finding neighborhoods include: smallest enclosing neighborhood (SEN), smallest enclosing inclusive neighborhood (SENR), natural ...

2. Various Methods of Estimation under Severe Multicollinearity Conditions: In what follows, we give a brief account of some important methods of estimation under severe multicollinearity conditions: (i). The Restricted Least Squares (RLS) Estimator of β : If we can put some restriction on the linear combination of regression coefficients such that , R r β = then the RLS estimator of β denoted ...

Estimation of density ridges has been gathering a great deal of attention since it enables us to reveal lower-dimensional structures hidden in data. Recently, subspace constrained mean shift (SCMS) was proposed as a practical algorithm for density ridge estimation. A key technical ingredient in SCMS is to accurately estimate the ratios of the density derivatives to the density. SCMS takes a thr...

This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regressio...