Results 91 to 100 of about 33,887 (293)

KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS

open access: yesE-Jurnal Matematika, 2014
Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached.
HANY DEVITA   +2 more
doaj   +1 more source

Multi-task Regression using Minimal Penalties [PDF]

open access: yes, 2011
In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this problem shows that the key element appearing for an optimal calibration is ...
Arlot, Sylvain   +2 more
core   +6 more sources

In Vivo Skin 3‐D Surface Reconstruction and Wrinkle Depth Estimation Using Handheld High Resolution Tactile Sensing

open access: yesAdvanced Healthcare Materials, EarlyView.
A compact handheld GelSight probe reconstructs in vivo 3‐D skin topography with micron‐level precision using a custom elastic gel and a learning‐based surface normal to height map pipeline. The device quantifies wrinkle depth across various body locations and detects changes in wrinkle depth following moisturizer application.
Akhil Padmanabha   +12 more
wiley   +1 more source

Difference based Ridge and Liu type Estimators in Semiparametric Regression Models [PDF]

open access: yes
We consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε.
Esra Akdeniz Duran   +2 more
core  

METODE REGRESI RIDGE UNTUK MENGATASI MULTIKOLINIERITAS PADA INDIKATOR KONSTRUKSI DI PROVINSI ACEH [PDF]

open access: yes, 2017
ABSTRAKPenelitian ini dilakukan untuk mengamati hubungan yang terjadi antara indikator-indikator konstruksi (sebagai variabel bebas), dan nilai konstruksi (sebagai variabel tak bebas).
Siti Zakirah
core  

3D‐Printed Gastrointestinal Stents: In Vivo Evaluation in a Swine Small Bowel Perforation Model

open access: yesAdvanced Healthcare Materials, EarlyView.
Gastrointestinal fistulae and perforations can lead to severe complications including sepsis and patient death. In this work, the efficacy of 3D‐printed gastrointestinal stents composed of poly‐lactic‐acid (PLA) was evaluated in an in vivo swine model.
Gweniviere Capron   +9 more
wiley   +1 more source

A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models

open access: yes, 2020
Constructing confidence intervals for the coefficients of high-dimensional sparse linear models remains a challenge, mainly because of the complicated limiting distributions of the widely used estimators, such as the lasso.
Li, Jingyi Jessica   +2 more
core   +1 more source

Generalized Mode and Ridge Estimation

open access: yes, 2014
The generalized density is a product of a density function and a weight function. For example, the average local brightness of an astronomical image is the probability of finding a galaxy times the mean brightness of the galaxy. We propose a method for studying the geometric structure of generalized densities.
Chen, Yen-Chi   +2 more
openaire   +2 more sources

POM‐Based Water Splitting Catalyst Under Acid Conditions Driven by Its Assembly on Carbon Nanotubes

open access: yesAdvanced Materials, EarlyView.
A newly‐engineered POM‐based electrocatalyst incorporating non‐innocent counter cations exhibits fast kinetics for either the OER or HER under strongly acidic conditions (1 m H2SO4), depending on whether it is assembled on carbon nanotubes (1@CNT) or physically mixed with them (1/CNT). In water‐splitting tests using a two‐electrode setup, these systems
Eugenia P. Quirós‐Díez   +8 more
wiley   +1 more source

A comparative study of the performance of new ridge estimators for multicollinearity: Insights from simulation and real data application

open access: yesAIP Advances
This paper addresses the challenge of multicollinearity in regression models, a condition that inflates the standard errors of coefficients, leading to unreliable estimates and wider confidence intervals.
Nadeem Akhtar, Muteb Faraj Alharthi
doaj   +1 more source

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