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Journal articles on the topic 'Ridge bias'

Author: Grafiati

Published: 30 November 2024

Last updated: 31 July 2025

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1

DEVITA, HANY, I. KOMANG GDE SUKARSA, and I. PUTU EKA N. KENCANA. "KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS." E-Jurnal Matematika 3, no. 4 (2014): 146. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p077.

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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. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation afte

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Tambunan, Ridho Febriansyah, and Suliadi. "Pemodelan New Ridge Regression Estimator pada Tingkat Kemiskinan di Kabupaten/Kota Provinsi Jawa Barat Tahun 2020." Bandung Conference Series: Statistics 2, no. 2 (2022): 317–23. http://dx.doi.org/10.29313/bcss.v2i2.4244.

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Abstract. Linear regression is a statistical method used to predict value dependent variable or response with one or more independent variables. If there is more than one predictor variable, multiple linear regression analysis is used. Ridge regression estimator has been introduced as an alternative to the ordinary least squares estimator (OLS) in the presence of multicollinearity. Ridge regression minimizes the mean square residual by introducing a bias constant and produced biased but stable coefficients estimate. The aim of this research is to apply a method introducing by Al-hassan (2010)

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Xiao, Penghao, Juliana Duncan, Liang Zhang, and Graeme Henkelman. "Ridge-based bias potentials to accelerate molecular dynamics." Journal of Chemical Physics 143, no. 24 (2015): 244104. http://dx.doi.org/10.1063/1.4937393.

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4

Harini, Sri. "Pendeteksian Outlier dengan Metode Regresi Ridge." CAUCHY 1, no. 1 (2009): 7. http://dx.doi.org/10.18860/ca.v1i1.1699.

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Dalam analisis regresi linier berganda adanya satu atau lebih pengamatan pencilan (outlier) akan menimbulkan dilema bagi para peneliti. Keputusan untuk menghilangkan pencilan tersebut harus dilandasi alasan yang kuat, karena kadang-kadang pencilan dapat memberikan informasi penting yang diperlukan. Masalah outlier ini dapat diatasi dengan berbagai metode, diantaranya metode regresi ridge (ridge regression). Untuk mengetahui kekekaran regresi ridge perlu melihat nilai-nilai R2, PRESS, serta leverage (hii), untuk metode regresi ridge dengan berbagai nilai tetapan bias k yang dipilih.

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Yanagihara, Hirokazu, Isamu Nagai, and Kenichi Satoh. "A Bias-Corrected Cp Criterion for Optimizing Ridge Parameters in Multivariate Generalized Ridge Regression." Japanese Journal of Applied Statistics 38, no. 3 (2009): 151–72. http://dx.doi.org/10.5023/jappstat.38.151.

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Kelly, R. J. "GDOP, Ridge Regression and the Kalman Filter." Journal of Navigation 43, no. 03 (1990): 409–27. http://dx.doi.org/10.1017/s0373463300014041.

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Multicollinearity and its effect on parameter estimators such as the Kalman filter is analysed using the navigation application as a special example. All position-fix navigation systems suffer loss of accuracy when their navigation landmarks are nearly collinear. Nearly collinear measurement geometry is termed the geometric dilution of position (GDOP). Its presence causes the errors of the position estimates to be highly inflated. In 1970 Hoerl and Kennard developed ridge regression to combat near collinearity when it arises in the predictor matrix of a linear regression model. Since GDOP is m

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Tian, Wei, Gaoming Huang, Huafu Peng, Xuebao Wang, and Xiaohong Lin. "Sensor Bias Estimation Based on Ridge Least Trimmed Squares." IEEE Transactions on Aerospace and Electronic Systems 56, no. 2 (2020): 1645–51. http://dx.doi.org/10.1109/taes.2019.2929973.

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Friendly, Michael. "The Generalized Ridge Trace Plot: Visualizing Bias and Precision." Journal of Computational and Graphical Statistics 22, no. 1 (2012): 50–68. http://dx.doi.org/10.1080/10618600.2012.681237.

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Arashi, M., M. Roozbeh, N. A. Hamzah, and M. Gasparini. "Ridge regression and its applications in genetic studies." PLOS ONE 16, no. 4 (2021): e0245376. http://dx.doi.org/10.1371/journal.pone.0245376.

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With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer

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Xu, Jianwen, and Hu Yang. "Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors." Acta et Commentationes Universitatis Tartuensis de Mathematica 15, no. 1 (2020): 27–43. http://dx.doi.org/10.12697/acutm.2011.15.03.

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In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the proposed estimators are derived and compared. Sufficient conditions on the departure parameter ∆ and the ridge parameter k are derived for the proposed estimators to be superior to the almost unbiased r

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Zuliana, Sri Utami. "PENENTUAN MODEL TERBAIK REGRESI RIDGE DAN TERAPANNYA." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 2 (2018): 43. http://dx.doi.org/10.20884/1.jmp.2018.10.2.2843.

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Ridge regression is one of penalized regression methods. Penalized regression methods are usually used for solving the problem of multicollinearity. The best model in ridge regression has been chosen by some previous techniques. In the techniques there is bias-variance trade-off. In this paper, Schall algorithm will be applied for choosing the best model. Schall algorithm is faster because it only needs a few iteratives to be convergence.

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Qasim, Muhammad, Kristofer Månsson, Muhammad Amin, B. M. Golam Kibria, and Pär Sjölander. "Biased Adjusted Poisson Ridge Estimators-Method and Application." Iranian Journal of Science and Technology, Transactions A: Science 44, no. 6 (2020): 1775–89. http://dx.doi.org/10.1007/s40995-020-00974-5.

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AbstractMånsson and Shukur (Econ Model 28:1475–1481, 2011) proposed a Poisson ridge regression estimator (PRRE) to reduce the negative effects of multicollinearity. However, a weakness of the PRRE is its relatively large bias. Therefore, as a remedy, Türkan and Özel (J Appl Stat 43:1892–1905, 2016) examined the performance of almost unbiased ridge estimators for the Poisson regression model. These estimators will not only reduce the consequences of multicollinearity but also decrease the bias of PRRE and thus perform more efficiently. The aim of this paper is twofold. Firstly, to derive the me

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Remilekun Enitan Alabi, Olatayo Olusegun Alabi, and Oluwadare O. Ojo. "Development of hybrid ridge–PCA estimators for addressing Multicollinearity in Gaussian linear regression models." World Journal of Advanced Research and Reviews 27, no. 1 (2025): 942–57. https://doi.org/10.30574/wjarr.2025.27.1.2559.

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This study tackles the persistent issue of multicollinearity in Gaussian linear regression which undermines the efficiency of Ordinary Least Squares (OLS) estimators. While Ridge Regression and Principal Component Analysis (PCA) are common remedies, they have limitations in terms of bias control and interpretability. To address this, the research proposes hybrid Ridge – PCA estimators using four newly developed ridge parameters combined with PCA. A Monte Carlo simulation evaluated 21 estimators including OLS, Ridge, PCA, and Liu estimators under varying sample sizes, error variances and multic

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Liu, Chaolin, Hu Yang, and Jibo Wu. "On the Weighted Mixed Almost Unbiased Ridge Estimator in Stochastic Restricted Linear Regression." Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/902715.

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We introduce the weighted mixed almost unbiased ridge estimator (WMAURE) based on the weighted mixed estimator (WME) (Trenkler and Toutenburg 1990) and the almost unbiased ridge estimator (AURE) (Akdeniz and Erol 2003) in linear regression model. We discuss superiorities of the new estimator under the quadratic bias (QB) and the mean square error matrix (MSEM) criteria. Additionally, we give a method about how to obtain the optimal values of parameterskandw. Finally, theoretical results are illustrated by a real data example and a Monte Carlo study.

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Anjani, Syarli Dita, Widiarti, Bernadhita Herindri Samodera Utami, Mustofa Usman, and Vitri Aprilla Handayani. "Georaphically Weighted Ridge Regression Modelling on 2023 Poverty Indicators Data in the Provinces of West Kalimantan and Central Kalimantan." Integra: Journal of Integrated Mathematics and Computer Science 1, no. 3 (2024): 73–80. https://doi.org/10.26554/integrajimcs.20241320.

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Regression analysis is a method to explain the relations between independent variables and a dependent variable. Linear regression analysis relies on certain assumptions, one of the assumption is homogeneity. However, there is a situation when the variance at each observation differs or called spatial heterogeneity.This issue can be solved using Geographically Weighted Regression (GWR), a statistical method that can be fixed spatial heterogeneity by adding a local weighted matrix, the result in GWR model is a local model for each observation point. However, GWR has a limitation, it cannot hand

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Andini, Agita, Etis Sunandi, Pepi Novianti, Idhia Sriliana, and Winalia Agwil. "Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka." Limits: Journal of Mathematics and Its Applications 22, no. 1 (2025): 77–84. https://doi.org/10.12962/limits.v22i1.3374.

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Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt t

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Saputri, Gustina, Netti Herawati, Tiryono Ruby, and Khoirin Nisa. "Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator." Jambura Journal of Mathematics 6, no. 2 (2024): 147–51. http://dx.doi.org/10.37905/jjom.v6i2.24828.

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Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. M

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Tyzhnenko, A. G., and Y. V. Ryeznik. "Practical Treatment of the Multicollinearity: The Optimal Ridge Method and the Modified OLS." PROBLEMS OF ECONOMY 1, no. 47 (2021): 155–68. http://dx.doi.org/10.32983/2222-0712-2021-1-155-168.

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The paper discusses the applicability of the two main methods for solving the linear regression (LR) problem in the presence of multicollinearity – the OLS and the ridge methods. We compare the solutions obtained by these methods with the solution calculated by the Modified OLS (MOLS) [1; 2]. Like the ridge, the MOLS provides a stable solution for any level of data collinearity. We compare three approaches by using the Monte Carlo simulations, and the data used is generated by the Artificial Data Generator (ADG) [1; 2]. The ADG produces linear and nonlinear data samples of arbitrary size, whic

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AL- Aabdi, Fadhil Abbul Abbas, and Rafid Malik Atiyah AL â€“ Shaibani. "Robust Estimators of Logistic Regression with Problems Multicollinearity or Outliers Values." Journal of Kufa for Mathematics and Computer 2, no. 2 (2014): 63–70. http://dx.doi.org/10.31642/jokmc/2018/0202010.

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Whenever there is a relationship between the explanatory variables (X_S). This relationship causes multicollinearity which in turn leads to inaccurate and bias estimations of the model parameters.  Therefore, this results in high discrepancy that influences the next phase of the statistical inference where (OLS), method loses its features having the lowest variance.  Consequently, this paper concerns itself with figuring out methods that can be applied by researchers and those who are interested in this field to overcome this problem using (Ridge) method. Moreover, the paper seeks

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Ghareeb, et. al., Zainab Fadhil. "A comparative study between shrinkage methods (ridge-lasso) using simulation." Periodicals of Engineering and Natural Sciences (PEN) 11, no. 2 (2023): 36–47. https://doi.org/10.21533/pen.v11.i2.98.

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The general linear model is widely used in many scientific fields, especially biological ones. The Ordinary Least Squares (OLS) estimators for the coefficients of the general linear model are characterized by good specifications symbolized by the acronym BLUE (Best Linear Unbiased Estimator), provided that the basic assumptions for building the model under study are met. The failure to achieve one of the basic assumptions or hypotheses required to build the model can lead to the emergence of estimators with low bias and high variance, which results in poor performance in both prediction and ex

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Saied Ismaeel, Shelan, Habshah Midi, and Kurdistan M. Taher Omar. "A Remedial Measure of Multicollinearity in Multiple Linear Regression in the Presence of High Leverage Points." Sains Malaysiana 53, no. 4 (2024): 907–20. http://dx.doi.org/10.17576/jsm-2024-5304-14.

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The ordinary least squares (OLS) is the widely used method in multiple linear regression model due to tradition and its optimal properties. Nonetheless, in the presence of multicollinearity, the OLS method is inefficient because the standard errors of its estimates become inflated. Many methods have been proposed to remedy this problem that include the Jackknife Ridge Regression (JAK). However, the performance of JAK is poor when multicollinearity and high leverage points (HLPs) which are outlying observations in the X- direction are present in the data. As a solution to this problem, Robust J

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Karakoca, Aydın. "A New Type Iterative Ridge Estimator: Applications and Performance Evaluations." Journal of Mathematics 2022 (May 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/3781655.

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The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error. Choosing the optimal value of the biasing parameter k is vital in ridge regression in terms of bias-variance trade off. Since the theoretical comparisons among the ridge estimators are not possible, it is general practice to carry out a Monte Carlo study to compare them. When the Monte Carlo designs on the existing ridge estimators are e

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Okamura, Hiroshi, Yuuho Yamashita, and Momoko Ichinokawa. "Ridge virtual population analysis to reduce the instability of fishing mortalities in the terminal year." ICES Journal of Marine Science 74, no. 9 (2017): 2427–36. http://dx.doi.org/10.1093/icesjms/fsx089.

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Abstract Tuned virtual population analyses are widely used for fisheries stock assessments. However, accurately estimating abundances and fishing mortality coefficients in the terminal year using tuned virtual population analyses is generally difficult, particularly when there is a limited number of available abundance indices. We propose a new method of integrating the tuned virtual population analyses with a ridge regression approach. In our method, penalization in the ridge regression is applied to the age-specific fishing mortalities in the terminal year, and the penalty parameter is autom

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Wang, Li, Zhiguo Fu, Yingcong Zhou, and Zili Yan. "The Implicit Regularization of Momentum Gradient Descent in Overparametrized Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10149–56. http://dx.doi.org/10.1609/aaai.v37i8.26209.

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The study of the implicit regularization induced by gradient-based optimization in deep learning is a long-standing pursuit. In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) in the continuous-time view, so-called momentum gradient flow (MGF). We show that the components of weight vector are learned for a deep linear neural networks at different evolution rates, and this evolution gap increases with the depth. Firstly, we show that if the depth equals one, the evolution gap between the weight vector components is linear, which is consistent wi

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GUIMARÃES, PAULO ROBERTO, OSVALDO CANDIDO, and ANDRÉ RONZANI. "REGULARIZATION METHODS FOR ESTIMATING A MULTI-FACTOR CORPORATE BOND PRICING MODEL: AN APPLICATION FOR BRAZIL." Annals of Financial Economics 16, no. 01 (2021): 2150005. http://dx.doi.org/10.1142/s2010495221500056.

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The present work focused on studying which factors affect Brazilian inflation-linked corporate bond prices in a primary market setting. The explanatory variables tested were rating, maturity, duration, issuer governance level, industrial classification, collateral, tax exemption, public offering modality, financial volume, coupon frequency, number of issues, number of days since going public, and the Brazilian basic interest rate target. In order to choose the set of variables with best predictive performance, best subsets ordinary least square (OLS) and least absolute shrinkage and selection

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Fok, Melissa Rachel, George Pelekos, and Lijian Jin. "Efficacy of Alveolar Ridge Preservation in Periodontally Compromised Molar Extraction Sites: A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 13, no. 5 (2024): 1198. http://dx.doi.org/10.3390/jcm13051198.

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Aim: To investigate the efficacy of alveolar ridge preservation (ARP) in periodontally compromised molar extraction sites. Methods: An electronic search was performed on 10th November 2023 across five databases, seeking randomised/non-randomised controlled trials (RCTs/NCTs) that included a minimum follow-up duration of four months. The RoB2 and Robins-I tools assessed the risk of bias for the included studies. Data on alveolar ridge dimensional and volumetric changes, keratinized mucosal width, and need for additional bone augmentation for implant placement were collected. Subsequently, a met

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Novitasari, Fitriana, Suliadi Suliadi, and Anneke Iswani A. "Kombinasi Regresi Tak Bias Ridge dengan Regresi Komponen Utama untuk Mengatasi Masalah Multikolinieritas." STATISTIKA: Journal of Theoretical Statistics and Its Applications 17, no. 1 (2017): 25–31. http://dx.doi.org/10.29313/jstat.v17i1.2713.

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Adewoye, Kunle Bayo, Ayinla Bayo Rafiu, Titilope Funmilayo Aminu, and Isaac Oluyemi Onikola. "INVESTIGATING THE IMPACT OF MULTICOLLINEARITY ON LINEAR REGRESSION ESTIMATES." MALAYSIAN JOURNAL OF COMPUTING 6, no. 1 (2021): 698. http://dx.doi.org/10.24191/mjoc.v6i1.10540.

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Multicollinearity is a case of multiple regression in which the predictor variables are themselves highly correlated. The aim of the study was to investigate the impact of multicollinearity on linear regression estimates. The study was guided by the following specific objectives, (i) to examined the asymptotic properties of estimators and (ii) to compared lasso, ridge, elastic net with ordinary least squares. The study employed Monte-carlo simulation to generate set of highly collinear and induced multicollinearity variables with sample sizes of 25, 50, 100, 150, 200, 250, 1000 as a source of

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Opoku, Eugene A., Syed Ejaz Ahmed, and Farouk S. Nathoo. "Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application." Entropy 23, no. 10 (2021): 1348. http://dx.doi.org/10.3390/e23101348.

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In a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimation strategy for high-dimensional data application, where the dimensions of the parameters are larger than the number of observations. In this paper, we are interested in estimating the fixed effects parameters of the LMM when it is assumed that some prior information is available in the form of linear restrictions on the parameters. We propose the p

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Balli, Gabriella, Andreas Ioannou, Charles A. Powell, Nikola Angelov, Georgios E. Romanos, and Nikolaos Soldatos. "Ridge Preservation Procedures after Tooth Extractions: A Systematic Review." International Journal of Dentistry 2018 (July 3, 2018): 1–7. http://dx.doi.org/10.1155/2018/8546568.

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Background. The purpose of this systematic review was to accurately assess the procedural success of ridge preservation technique through the application of strict inclusion and exclusion criteria. Data Sources. A methodical search of PubMed of the US National Library of Medicine and the Cochrane Central Register of Controlled Trials was conducted for applicable articles. Only randomized controlled trials comparing ridge preservation treatment with a nongrafting control, ten-subject minimum sample size, and three or more months of follow-up were included in our study. Types of Studies Reviewed

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Huang, X., A. J. Seeds, and J. S. Roberts. "Reverse bias tuned multiple quantum well ridge guide laser with uniform frequency modulation response." Applied Physics Letters 71, no. 6 (1997): 765–66. http://dx.doi.org/10.1063/1.119639.

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Qona’ah, Niswatul, Sutikno, Kiki Ferawati, and Muhammad Bayu Nirwana. "Temperature Forecast Using Ridge Regression as Model Output Statistics." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): 383–88. http://dx.doi.org/10.14421/icse.v3.533.

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Over the past few years, BMKG (Meteorological, Climatological and Geophysical Agency) in Indonesia has used numerical weather forecasting techniques, namely Numerical Weather Prediction (NWP). However, the NWP forecast still has a high bias because it is only measured on a global scale and unable to capture the dynamics of atmosphere (Wilks, 2007). Hence, this study implements Ridge Regression as Model Output Statistics (MOS) for temperature forecast. This study uses the maximum temperature (Tmax) and minimum temperature (Tmin) observation at 4 stations in Indonesia as the response variables a

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MA, HONG, XINJIAN YI та SIHAI CHEN. "1.3 μm AlGaInAs-InP POLARIZATION-INSENSITIVE SEMICONDUCTOR OPTICAL AMPLIFIER WITH TENSILE STRAINED WELLS GROWN BY MOVPE". International Journal of Nanoscience 02, № 03 (2003): 119–23. http://dx.doi.org/10.1142/s0219581x03001024.

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We demonstrate a polarization-insensitive multiple-quantum-well optical amplifer for 1.3 μm wavelength in AlGaInAs-InP material system, using three tensile strained wells with strain of 0.36% in the active region. The amplifiers were fabricated forming ridge waveguide structure, which showed excellent polarization insensitivity (less than 0.6 dB) over the entire range of wavelength (1.28 μm ~ 1.34 μm) and a gain of 22.5 dB at the bias current of 200 mA and 1304 nm wavelength.

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Wang, Jingyu, Xiquan Dong, Aaron Kennedy, Brooke Hagenhoff, and Baike Xi. "A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF." Weather and Forecasting 34, no. 4 (2019): 805–31. http://dx.doi.org/10.1175/waf-d-19-0025.1.

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Abstract A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the Na

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Wasilaine, Trifena L., Mozart W. Talakua, and Yopi A. Lesnussa. "MODEL REGRESI RIDGE UNTUK MENGATASI MODEL REGRESI LINIER BERGANDA YANG MENGANDUNG MULTIKOLINIERITAS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 8, no. 1 (2014): 31–37. http://dx.doi.org/10.30598/barekengvol8iss1pp31-37.

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Model Regresi Linier Berganda merupakan sebuah model yang digunakan untuk menganalisis hubungan antar variabel. Hubungan tersebut dapat diekspresikan dalam bentuk persamaan yang menghubungkan variabel terikat (Y) dengan beberapa variabel bebas (X). Jika adanya hubungan linier yang sempurna atau pasti diantara beberapa atau semua variabel bebas dari model Regresi Berganda disebut Multikolinieritas. Jika korelasi antara dua atau lebih variabel bebas dalam suatu persamaan regresi linier berganda ini terjadi maka taksiran koefisien dari variabel yang bersangkutan tidak lagi tunggal melainkan tidak

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Nagashima, Kaori, Aaron C. Birch, Jesper Schou, Bradley W. Hindman, and Laurent Gizon. "An improved multi-ridge fitting method for ring-diagram helioseismic analysis." Astronomy & Astrophysics 633 (January 2020): A109. http://dx.doi.org/10.1051/0004-6361/201936662.

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Context. There is a wide discrepancy in current estimates of the strength of convection flows in the solar interior obtained using different helioseismic methods applied to observations from the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory. The cause for these disparities is not known. Aims. As one step in the effort to resolve this discrepancy, we aim to characterize the multi-ridge fitting code for ring-diagram helioseismic analysis that is used to obtain flow estimates from local power spectra of solar oscillations. Methods. We updated the multi-ridge fitting code

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Oyeleke, Kamoru, Timothy O. Olatayo, and Biodun T. Efuwape. "Handling Multicollinearity and Outliers: A Comparative Study of Some One and Two–Parameter Estimators Using Real-Life Data." International Journal of Development Mathematics (IJDM) 1, no. 4 (2024): 177–90. https://doi.org/10.62054/ijdm/0104.14.

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It is evident that when data suffers the problem of multicollinearity, the traditional least square is incapacitated and unreliable. Hence, needs to use bias estimator such as Ridge estimator, Liu estimator among others. Also, presence of outliers is another treat and to tackle this challenge is the use of robust regression estimators which include M, MM, LTS, LMS, LAD, LQS and S estimators. However, the presence of the two anomalies may be inevitable. Several estimators have been combined to handle the problems simultaneously. Therefore, this study compared and contrasted some robust one and

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Li, Yuhan. "Stock Price Prediction based on Multiple Regression Models." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 657–62. http://dx.doi.org/10.54097/hset.v39i.6622.

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Over the past two years, global stock markets have gradually recovered and new investors have entered the market. While there are many factors affecting stock prices and the stock market is changing rapidly, the way to accurately predict stock prices has become the focus of investors. This paper will use the concept of machine learning to predict the stock prices of three listed companies based on three different regression models (i.e., OLS, Ridge and XGBoost). According to the analysis, the OLS model and the Ridge model are very accurate in predicting stock prices, especially in the low and

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Li, Ruoyu, Qin Deng, Dong Tian, Daoye Zhu, and Bin Lin. "Predicting Perovskite Performance with Multiple Machine-Learning Algorithms." Crystals 11, no. 7 (2021): 818. http://dx.doi.org/10.3390/cryst11070818.

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Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation ene

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Chavda, Suraj, and Liran Levin. "Human Studies of Vertical and Horizontal Alveolar Ridge Augmentation Comparing Different Types of Bone Graft Materials: A Systematic Review." Journal of Oral Implantology 44, no. 1 (2018): 74–84. http://dx.doi.org/10.1563/aaid-joi-d-17-00053.

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Alveolar ridge augmentation can be completed with various types of bone augmentation materials (autogenous, allograft, xenograft, and alloplast). Currently, autogenous bone is labeled as the “gold standard” because of faster healing times and integration between native and foreign bone. No systematic review has currently determined whether there is a difference in implant success between various bone augmentation materials. The purpose of this article was to systematically review comparative human studies of vertical and horizontal alveolar ridge augmentation comparing different types of bone

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Habtemariam, Getachew Mekuria, Sudhir Kumar Mohapatra, and Hussien Worku Seid. "Software reliability prediction using ensemble learning with random hyperparameter optimization." Review of Computer Engineering Research 11, no. 1 (2024): 1–15. http://dx.doi.org/10.18488/76.v11i1.3597.

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The paper investigates software reliability prediction by using ensemble learning with random hyperparameter optimization. Software reliability is a significant problem with software quality that developers face. It involves accurately predicting the next failure. In recent years, machine learning techniques and ensemble learning approaches have been applied to improve software reliability prediction. These approaches aim to analyze historical data and develop models that can accurately forecast when failures are likely to occur. The article proposes an ensemble learning regression model using

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Guan, Ying, and Guang-Hui Fu. "A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression." Mathematics 10, no. 20 (2022): 3824. http://dx.doi.org/10.3390/math10203824.

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When developing prediction models for small or sparse binary data with many highly correlated covariates, logistic regression often encounters separation or multicollinearity problems, resulting serious bias and even the nonexistence of standard maximum likelihood estimates. The combination of separation and multicollinearity makes the task of logistic regression more difficult, and a few studies addressed separation and multicollinearity simultaneously. In this paper, we propose a double-penalized method called lFRE to combat separation and multicollinearity in logistic regression. lFRE combi

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Anderson, Kelly J., and John H. Kalivas. "Assessment of Pareto Calibration, Stability, and Wavelength Selection." Applied Spectroscopy 57, no. 3 (2003): 309–16. http://dx.doi.org/10.1366/000370203321558227.

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Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models ar

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Zhou, Peigen, Chen Wang, Jin Sun, Zhe Chen, Jixin Chen, and Wei Hong. "A 66–76 GHz Wide Dynamic Range GaAs Transceiver for Channel Emulator Application." Micromachines 13, no. 5 (2022): 809. http://dx.doi.org/10.3390/mi13050809.

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In this study, we developed a single-channel channel emulator module with an operating frequency covering 66–67 GHz, including a 66–76 GHz wide dynamic range monolithic integrated circuit designed based on 0.1 µm pHEMT GaAs process, a printed circuit board (PCB) power supply bias network, and low-loss ridge microstrip line to WR12 (60–90 GHz) waveguide transition structure. Benefiting from the on-chip multistage band-pass filter integrated at the local oscillator (LO) and radio frequency (RF) ends, the module’s spurious components at the RF port were greatly suppressed, making the module’s out

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QONA’AH, NISWATUL, HASIH PRATIWI, and YULIANA SUSANTI. "MODEL OUTPUT STATISTICS DENGAN PRINCIPAL COMPONENT REGRESSION, PARTIAL LEAST SQUARE REGRESSION, DAN RIDGE REGRESSION UNTUK KALIBRASI PRAKIRAAN CUACA JANGKA PENDEK." Jurnal Matematika UNAND 10, no. 3 (2021): 355. http://dx.doi.org/10.25077/jmu.10.3.355-368.2021.

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Penelitian ini merupakan upaya pengembangan Model Output Statistics (MOS) yang akan digunakan sebagai alat kalibrasi prakiraan cuaca jangka pendek. Informasi mengenai prakiraan cuaca yang akurat diharapkan dapat meminimalkan risiko kecelakaan yang disebabkan oleh cuaca, khususnya dalam bidang transportasi udara dan laut. Metode yang akan dikembangkan mencakup beberapa stasiun pengamatan cuaca di Indonesia. MOS merupakan sebuah metode berbasis regresi yang mengoptimalkan hubungan antara observasi cuaca dan luaran model Numerical Weather Predictor (NWP). Beberapa masalah yang muncul kaitannya de

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Molina-Barahona, Magdalena, Bolívar Delgado-Gaete, Denia Morales-Navarro, Joaquín Urbizo-Vélez, and Renata Avecillas-Rodas. "Imaging Evaluation of Platelet-Rich Fibrin in Post-Exodontic Bone Regeneration: A Systematic Review." Dentistry Journal 11, no. 12 (2023): 277. http://dx.doi.org/10.3390/dj11120277.

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Tooth extraction is the most common procedure in dental practice. However, in the long term, it may cause alveolar ridge atrophy. This systematic review aimed to evaluate the role of platelet-rich fibrin (PRF) in post-exodontic alveolar ridge preservation in terms of its effectiveness in the regeneration of bone tissue as assessed by imaging and its efficacy compared to physiological bone healing. The study is presented in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. This systematic review was conducted using electronic databases s

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Ralston, J. D., S. Weisser, K. Eisele, et al. "Low-bias-current direct modulation up to 33 GHz in InGaAs/GaAs/AlGaAs pseudomorphic MQW ridge-waveguide lasers." IEEE Photonics Technology Letters 6, no. 9 (1994): 1076–79. http://dx.doi.org/10.1109/68.324673.

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Ye, F., D. Moss, J. G. Simmons, et al. "A four-channel ridge wave-guide quantum well wavelength division demultiplexing detector and its optimization." Canadian Journal of Physics 70, no. 10-11 (1992): 931–36. http://dx.doi.org/10.1139/p92-148.

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We present a four-channel wavelength division demultiplexing detector using the principle of quantum confined Stark effect. This device is based on a ridge waveguide GaAs–AlGaAs single quantum well graded index separate confinement heterostructure. Four detectors are fabricated sequentially along the wave guide and their band gaps are tuned to progressively smaller values by applying progressively larger reverse bias voltages. Thus each detector responds preferably to one of the four input wavelengths. For transverse electric polarization, better than −10 dB crosstalk was achieved with a 14 nm

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Zängl, Günther. "Interaction between Dynamics and Cloud Microphysics in Orographic Precipitation Enhancement: A High-Resolution Modeling Study of Two North Alpine Heavy-Precipitation Events." Monthly Weather Review 135, no. 8 (2007): 2817–40. http://dx.doi.org/10.1175/mwr3445.1.

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Abstract Interactions of atmospheric dynamics and cloud microphysics with the Alpine orography are investigated for two north Alpine heavy-precipitation cases (20–22 May 1999 and 22–23 August 2005). Both cases were related to a deep cyclone propagating slowly eastward along the Alps, advecting moist air of Mediterranean origin toward the northern side of the Alps. A validation against high-resolution rain gauge data reveals that the average model bias is below 15% in the region of interest, but there is a tendency to systematically underestimate very heavy precipitation. A scale decomposition

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Hall, Rob A., Barbara Berx, and Gillian M. Damerell. "Internal tide energy flux over a ridge measured by a co-located ocean glider and moored acoustic Doppler current profiler." Ocean Science 15, no. 6 (2019): 1439–53. http://dx.doi.org/10.5194/os-15-1439-2019.

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Abstract. Internal tide energy flux is an important diagnostic for the study of energy pathways in the ocean, from large-scale input by the surface tide to small-scale dissipation by turbulent mixing. Accurate calculation of energy flux requires repeated full-depth measurements of both potential density (ρ) and horizontal current velocity (u) over at least a tidal cycle and over several weeks to resolve the internal spring–neap cycle. Typically, these observations are made using full-depth oceanographic moorings that are vulnerable to being “fished out” by commercial trawlers when deployed on

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