We consider two regularization approaches, the LASSO and the threshold-gradient-directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan-Meier weights to account for censoring in the least squares criterion. The weighted least squares objective fun...

two multivariate calibration methods are compared for the simultaneous chromatographic determination and separation of sulfamethoxazole (smx) and phthalazine (phz) by high performance liquid chromatography (hplc). multivariate calibration techniques such as classical least squares (cls) and inverse least squares (ils) were introduced into hplc to determine the quantification by using uv detecto...

This study presents an auditory cortical representation for passive sonar signal and use it to extract features to categorize an unknown signal as being of man-made or natural origin. A ridge partial least squares (RPLS) model is given to establish the decision criterion, and regression coefficients are used to search for salient regions of the auditory cortical representation related to the cl...

In this paper, we first get further consideration of the order perturbation with normwise condition number MTLS problem. For easy estimation, show a lower bound for which is proved to be optimal. overcome problems encountered in calculating number, give an upper computing more effectively and nonstandard unusual bounds Both two types can enjoy storage computational advantages. getting insight i...

Background: A new algorithm for precise characterisation of rotationally symmetric aspheric surfaces by the conic section and polynomial according to the ISO 10110 standard is described. Methods: The algorithm uses only the iterative linear least squares. It uses fitting the surface form in a combination with terms containing its spatial derivatives that represent infinitesimal transformations ...

This paper considers two recently proposed semiparametric estimators for distribution-free binary response models under a conditional median restriction. It shows that these estimators can be implemented in Stata using nl through simple modifications to the nonlinear least squares probit criterion function. We then introduce dfbr, a new Stata command which implements these estimators, and provi...

For the problem of estimation of Money demand model of Pakistan, money supply (M1) shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons a...