Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased available literature, including ridge, Least Angle Shrinkage Selection Operator (LASSO), and elastic net. In this work, we study performance classical LASSO, adaptive ordinary least squares (OLS) methods high-multicollinearity scenarios pro...

In instantaneous encephalogram or magnetoencephalogram (EEG/MEG) source analysis, ordinary least squares estimation (OLS) requires that the spatial noise is homoscedastic and uncorrelated over sensors. In spatio-temporal analysis OLS also requires that the noise is homoscedastic and uncorrelated in time (over samples). Generally, these assumptions are violated and, as a consequence, OLS can giv...

The graphical analysis uses an ordinary least squares (OLS) fitting of transformed data to determine the total volume of distribution (VT) and is not dependent upon a compartmental model configuration. This method, however, suffers from a noise-dependent bias. Approaches for reducing this bias include incorporating a presmoothing step, minimizing the squared perpendicular distance to the regres...

OBJECTIVES The 29-item Multiple Sclerosis Impact Scale (MSIS-29) is a psychometrically validated patient-reported outcome measure increasingly used in trials of treatments for multiple sclerosis. However, it is non-preference-based and not amenable for use across policy decision-making contexts. Our objective was to statistically map from the MSIS-29, version 2, to the EuroQol five-dimension (E...

Ordinary least squares (OLS) and stochastic frontier (SF) analyses are commonly used to estimate industry-level and firm-specific efficiency. Using cross-sectional data for English public hospitals, a total cost function based on a specification developed by the English Department of Health is estimated. Confidence intervals are calculated around the OLS residuals and around the inefficiency co...

Environmental pollution comes from several sectors of activities. It is almost conceivable that the education sector subsidies to disruption environmental quality. The study employs panel cointegration techniques and method moments quantile regression (MMQR) inspect influence income, (scholarly enrollment expenditure), foreign direct investment on carbon dioxide (CO 2 ) emissions for a 46 Belt ...