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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 (November 28, 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 after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.
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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 (July 29, 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) to obtaine the bias constant in ridge regression that produces smaller bias than method given by Hoerl & Kennad. We apply this method to model the poverty rate in districts/cities in West Java in 2020. The dependent variable (Y) is the proverty rate and the independet variables are (average length of school), (unemployment rate), (gross domestic regional product), (human development index), (number of labor force participation rate). The value of the ridge constant using the Al-hassan (2010) method is 1.377633. The ridge regression model for the standardized variables is with , & that significanly affect the reponse. The regression model based on the original variable is
Abstrak. Analisis regresi linier adalah metode statistika yang digunakan untuk membentuk model hubungan antara variabel terikat (dependent atau respon ) dengan satu atau lebih variabel bebas (independent atau prediktor). Apabila variabel prediktor lebih dari satu maka digunakan analisis regresi linier berganda. Ada beberapa asumsi yang harus terpenuhi dalam regresi linier berganda diantaranya asumsi multikolinearitas. Salah satu metode untuk mengatasi masalah multikolinieritas adalah menggunakan metode regresi ridge. Regresi ridge meminimumkan residual dengan menambahkan tetapan bias (k). Namun metode ini masih memiliki kelemahan yaitu masih terdapat bias. Untuk memperbaiki kelemahan tersebut Al-hassan mengajukan metode baru. Metode ini bertujuan untuk memperkecil nilai bias dari suatu penduga dengan cara memodifikasi nilai k. Dalam skripsi ini kami menerapkan metode tersebut untuk memodelkan tingkat kemiskinan di Kabupaten/Kota di Jawa Barat Tahun 2020. Variabel responnya adalah Y (tingkat kemiskinan) dan variabel bebasnya (lama rata-rata sekolah), (tingkat pengangguran terbuka), (produk domestik regional bruto), (indeks pembangunan manusia), (jumlah angkatan kerja). Nilai konstanta ridge menggunakan metode Al-hassan (2010) sebesar Sehingga didapatkan model persamaan ridge yaitu : Dengan variabel baku , dan varibel baku yang signifikan terhadap variabel . Dan model berdasarkan variabel aslinya adalah
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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 (December 28, 2015): 244104. http://dx.doi.org/10.1063/1.4937393.
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Harini, Sri. "Pendeteksian Outlier dengan Metode Regresi Ridge." CAUCHY 1, no. 1 (November 15, 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 (September 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 mathematically equivalent to a nearly collinear predictor matrix, Kelly suggested using ridge regression techniques in navigation signal processors to reduce the effects of GDOP. The original programme intended to use ridge regression not only to reduce variance inflation but also to reduce bias inflation. Reducing bias inflation is an extension of Hoerl's ridge concept by Kelly. Preliminary results show that ridge regression will reduce the effects of variance inflation caused by GDOP. However, recent results (Kelly) conclude it will not reduce bias inflation as it arises in the navigation problem, GDOP is not a mismatched estimator/model problem. Even with an estimator matched to the model, GDOP may inflate the MSE of the ordinary Kalman filter while the ridge recursive filter chooses a suitable biased estimator that will reduce the MSE. The main goal is obtaining a smaller MSE for the estimator, rather than minimizing the residual sum of squares. This is a different operation than tuning the Kalman filter's dynamic process noise covariance Q, in order to compensate for unmodelled errors. Although ridge regression has not yielded a satisfactory solution to the general GDOP problem, it has provided insight into exactly what causes multicollinearity in navigation signal processors such as the Kalman filter and under what conditions an estimator's performance can be improved.
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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 (April 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 (May 23, 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 (April 8, 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 about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.
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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 (December 11, 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 ridge estimator, restricted almost unbiased ridge estimator and preliminary test estimator. Furthermore, some graphical results are provided to illustrate theoretical results.
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Zuliana, Sri Utami. "PENENTUAN MODEL TERBAIK REGRESI RIDGE DAN TERAPANNYA." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 2 (December 28, 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 (October 3, 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 mean square error properties of the Modified Almost Unbiased PRRE (MAUPRRE) and Almost Unbiased PRRE (AUPRRE) and then propose new ridge estimators for MAUPRRE and AUPRRE. Secondly, to compare the performance of the MAUPRRE with the AUPRRE, PRRE and maximum likelihood estimator. Using both simulation study and real-world dataset from the Swedish football league, it is evidenced that one of the proposed, MAUPRRE ($$ \hat{k}_{q4} $$ k ^ q 4 ) performed better than the rest in the presence of high to strong (0.80–0.99) multicollinearity situation.
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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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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, which allows the investigation of the OLS equation's regularization problem. Two possible regularization versions are the COV version considered in [1; 2] and the ST version commonly used in the literature and practice. The performed investigations reveal that the ridge method in the COV version has an approximately constant optimal regularizer (?_ridge^((opt))?0.1) for any sample size and collinearity level. The MOLS method in this version also has an approximately constant optimal regularizer, but its value is significantly smaller (?_MOLS^((opt))?0.001). On the contrary, the ridge method in the ST version has the optimal regularizer, which is not a constant but depends on the sample size. In this case, its value needs to be set to ?_ridge^((opt))?0.1(n-1). With such a value of the ridge parameter, the obtained solution is strictly the same as one obtained with the COV version but with the optimal regularizer ?_ridge^((opt))?0.1 [1; 2]. With such a choice of the regularizer, one can use any implementation of the ridge method in all known statistical software by setting the regularization parameter ?_ridge^((opt))?0.1(n-1) without extra tuning process regardless of the sample size and the collinearity level. Also, it is shown that such an optimal ridge(0.1) solution is close to the population solution for a sample size large enough, but, at the same time, it has some limitations. It is well known that the ridge(0.1) solution is biased. However, as it has been shown in the paper, the bias is economically insignificant. The more critical drawback, which is revealed, is the smoothing of the population solution – the ridge method significantly reduces the difference between the population regression coefficients. The ridge(0.1) method can result in a solution, which is economically correct, i.e., the regression coefficients have correct signs, but this solution might be inadequate to a certain extent. The more significant the difference between the regression coefficients in the population, the more inadequate is the ridge(0.1) method. As for the MOLS, it does not possess this disadvantage. Since its regularization constant is much smaller than the corresponding ridge regularizer (0.001 versus 0.1), the MOLS method suffers little from both the bias and smoothing of its solutions. From a practical point of view, both the ridge(0.1) and the MOLS methods result in close stable solutions to the LR problem for any sample size and collinearity level. With the sample size increasing, both solutions approach the population solution. We also demonstrate that for a small sample size of less than 40, the ridge(0.1) method is preferable, as it is more stable. When the sample size is medium or large, it is preferable to use the MOLS as it is more accurate yet has approximately the same stability.
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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 (August 1, 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. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.
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AL- Aabdi, Fadhil Abbul Abbas, and Rafid Malik Atiyah AL – Shaibani. "Robust Estimators of Logistic Regression with Problems Multicollinearity or Outliers Values." Journal of Kufa for Mathematics and Computer 2, no. 2 (December 1, 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 to solve other problems such as the loss of normal distribution property or abnormalility by means of methodical means including (Ridge) and (Robust Ridge).
However this study is applied through simulation experiments aim at producing the data of the model. Based on these experiments and tests, the research has come up with the result that (Robust Ridge) is the best method that might be employed to solve the problem of has both normal and abnormal data for the estimation of the parameters of the Logistic Regression Model.
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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 (April 30, 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 Jackknife Ridge MM (RJMM) and Robust Jackknife Ridge GM2 (RJGM2) estimators are put forward. Nevertheless, they are still not very efficient because they suffer from long computational running time, some elements of biased and do not have bounded influence property. This paper proposes a robust Jackknife ridge regression that integrates a generalized M estimator and fast improvised Gt (GM-FIMGT) estimator, in its establishment. We name this method the robust Jackknife ridge regression based on GM-FIMGT, denoted as RJFIMGT. The numerical results show that the proposed RJFIMGT method was found to be the best method as it has the least values of RMSE and bias compared to other methods in this study.
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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 examined, it is seen that the performances of the ridge estimators are only considered for the same level of relationship between the independent variables. However, it is more likely to encounter different levels of relationships between the independent variables in real data sets. In this study, a new type iterative ridge estimator is proposed based on a modified form of the estimated mean square error function. Furthermore, a novel search algorithm is provided to achieve the estimations. The performance of the proposed estimator is compared with that of the ordinary least squares estimator and existing 18 ridge estimators through an extensive Monte Carlo design. In the design of the Monte Carlo, both data generation techniques were taken into account, based on the constant and varying correlation levels between the independent variables. Two illustrative real data examples are presented. The proposed estimator outperforms the existing estimators in the sense of the mean squared error for both data generating types. Moreover, it is also superior with respect to the k-fold cross-validation method in the real data examples.
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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 (May 26, 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 automatically selected by minimizing the retrospective bias. Therefore, our method is able to simultaneously obtain a stable estimation of fishing mortality coefficients in the terminal year and reduce retrospective bias. Simulation tests based on the northern Japan Sea stock of walleye pollock (Gadus chalcogrammus) in the Sea of Japan demonstrated that this method yielded less biased estimates of abundances and avoided overestimations of fishing mortality coefficients in the terminal year. In addition, despite limited abundance indices, our method can perform reliable abundance estimations even under hyperstability and hyperdepletion conditions.
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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 (June 26, 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 with the performance of ridge. In particular, we establish a tight coupling between MGF and ridge for the least squares regression. In detail, we show that when the regularization parameter of ridge is inversely proportional to the square of the time parameter of MGF, the risk of MGF is no more than 1.54 times that of ridge, and their relative Bayesian risks are almost indistinguishable. Secondly, if the model becomes deeper, i.e. the depth is greater than or equal to 2, the evolution gap becomes more significant, which implies an implicit bias towards sparse solutions. The numerical experiments strongly support our theoretical results.
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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 (March 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 operator (LASSO) were applied on a testing sample. For estimating purposes, we also tested the Ridge estimator. For both LASSO and Ridge, we used the k-fold approach to choose the optimal value for the lambda penalty. In terms of smallest mean squared error, the OLS estimator outperformed both the Ridge and the LASSO. This result suggests that the variance-bias trade-off might not be a concern for the Brazilian case.
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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 (July 18, 2017): 25–31. http://dx.doi.org/10.29313/jstat.v17i1.2713.
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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 (February 20, 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 meta-analysis was carried out to derive the pooled estimates. Results: Six studies were incorporated in the present review, and a total of 135 molar extraction sockets in 130 subjects were included in the meta-analysis. ARP was undertaken in 68 sites, and 67 sites healed spontaneously. The follow-up time ranged from 4 to 6 months. The meta-analysis of both RCTs and NCTs showed significant differences in mid-buccal ridge width changes at 1 mm level below ridge crest with a mean difference (MD) of 3.80 (95% CI: 1.67–5.94), mid-buccal ridge height changes (MD: 2.18; 95% CI: 1.25–3.12) and volumetric changes (MD: 263.59; 95% CI: 138.44–388.74) in favour of ARP, while the certainty of evidence is graded low to very low. Moreover, ARP appeared to reduce the need for additional sinus and bone augmentation procedures at implant placement with low certainty of evidence. Conclusions: Within the limitations of this study, alveolar ridge preservation in periodontally compromised extraction sites may, to some extent, preserve the ridge vertically and horizontally with reference to spontaneous healing. However, it could not eliminate the need for additional augmentation for implant placement. Further, longitudinal studies with large sample sizes and refined protocols are needed.
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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 (March 9, 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 data in this research work and the data was analyzed with lasso, ridge, elastic net and ordinary least squares using statistical package. The study findings revealed that absolute bias of ordinary least squares was consistent at all sample sizes as revealed by past researched on multicollinearity as well while lasso type estimators were fluctuate alternately. Also revealed that, mean square error of ridge regression was outperformed other estimators with minimum variance at small sample size and ordinary least squares was the best at large sample size. The study recommended that ols was asymptotically consistent at a specified sample sizes on this research work and ridge regression was efficient at small and moderate sample size.
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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. In a screening between January 1980 and September 2017, articles meeting predetermined criteria were further examined in a qualitative data analysis. A thorough search of the databases provided 1876 articles. Of these records, 174 were assessed for eligibility through the systematic employment of inclusion and exclusion criteria. Results. Two records were appropriate for further data analysis. One study used a mixture of a deproteinized cancellous bovine bone and porcine collagen fibers in a block form (DBB/CF), while the other study used leukocyte-platelet-rich fibrin (L-PRF). The use of DBB/CF reduced the magnitude of vertical bone resorption, yet the study showed high risk of bias. The use of L-PRF reduced the magnitude of both the horizontal and vertical crestal bone resorption; however, the low sample size created wide standard deviations between the test and control groups. Inherent weaknesses were present in both studies. Through methodical analysis of both records, the dissimilarities prevented the conduction of a meta-analysis. Implications of Key Findings. Within the limitations of this systematic review, L-PRF reduced the magnitude of vertical and horizontal bone resorption, which places L-PRF as a potential material of choice for ridge preservation procedures. Conclusions. Within the limitations and weaknesses of both studies, the use of DBB/CF prevented the vertical crestal bone resorption while the L-PRF prevented both the horizontal and vertical crestal bone resorption. More randomized controlled clinical trials are needed to eliminate all the confounding factors, which bias the outcome of ridge preservation techniques.
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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 (October 15, 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 pretest and shrinkage estimation strategies using the ridge full model as the base estimator. We establish the asymptotic distributional bias and risks of the suggested estimators and investigate their relative performance with respect to the ridge full model estimator. Furthermore, we compare the numerical performance of the LASSO-type estimators with the pretest and shrinkage ridge estimators. The methodology is investigated using simulation studies and then demonstrated on an application exploring how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease.
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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 (August 11, 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 and NWP as the predictor variable. The results show that the performance of the model based on Root Mean Square Error of Prediction (RMSEP) is considered to be good and intermediate. The RMSEP for Tmax in all stations is intermediate (0.9-1.2), Tmin in all stations is good (0.5-0.8). The prediction result from Ridge Regression is more accurate than the NWP model and able to correct up to 90.49% of the biased NWP for Tmax forecasting.
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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 (July 2, 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 National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.
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MA, HONG, XINJIAN YI, and SIHAI CHEN. "1.3 μm AlGaInAs-InP POLARIZATION-INSENSITIVE SEMICONDUCTOR OPTICAL AMPLIFIER WITH TENSILE STRAINED WELLS GROWN BY MOVPE." International Journal of Nanoscience 02, no. 03 (June 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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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 (March 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 terhingga banyaknya sehingga tidak mungkin lagi menduganya. Dalam kasus ini peneliti akan melihat hubungan antara variabel-variabelnya. Apabila terdapat hubungan antara variabel-variabel bebasnya. Maka akan diterapkan metode Regresi Ridge untuk menstabilkan nilai koefisien regresi karena adanya Multikolinieritas. Regresi Ridge merupakan metode estimasi koefisien regresi yang diperoleh melalui penambahan konstanta bias 𝑐 pada diagonal 𝑋𝑇𝑋. Sehingga diperoleh persamaan regresi linier yang baru dan tidak mengandung multikolinieritas.
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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 developed by Greer et al. (2014, Sol. Phys., 289, 2823) to solve several problems we identified through our inspection of the code. In particular, we changed the (1) merit function to account for the smoothing of the power spectra, (2) model for the power spectrum, and (3) noise estimates. We used Monte Carlo simulations to generate synthetic data and to characterize the noise and bias of the updated code by fitting these synthetic data. Results. The bias in the output fit parameters, apart from the parameter describing the amplitude of the p-mode resonances in the power spectrum, is below what can be measured from the Monte-Carlo simulations. The amplitude parameters are underestimated; this is a consequence of choosing to fit the logarithm of the averaged power. We defer fixing this problem as it is well understood and not significant for measuring flows in the solar interior. The scatter in the fit parameters from the Monte-Carlo simulations is well-modeled by the formal error estimates from the code. Conclusions. We document and demonstrate a reliable multi-ridge fitting method for ring-diagram analysis. The differences between the updated fitting results and the original results are less than one order of magnitude and therefore we suspect that the changes will not eliminate the aforementioned orders-of-magnitude discrepancy in the amplitude of convective flows in the solar interior.
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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 middle price ranges. In contrast to these typical linear regression models, the XGBoost model is not as accurate in predicting stock prices and even has a significant prediction bias in the high price range. These results will enable subsequent research to make better choices when selecting models for forecasting, especially for data sets with different characteristics.
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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 (July 14, 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 energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.
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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 (February 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 graft materials (autogenous, allograft, xenograft, and alloplast). A MEDLINE search was conducted under the 3 search concepts of bone augmentation, dental implants, and alveolar ridge augmentation. Studies pertaining to socket grafts or sinus lifts were excluded. Case reports, small case series, and review papers were excluded. A bias assessment tool was applied to the final articles. Overall, 219 articles resulted from the initial search, and 9 articles were included for final analysis. There were no discernible differences in implant success between bone augmentation materials. Generally, patients preferred nonautogenous bone sources as there were fewer hospital days, less pain, and better recovery time. Two articles had industrial support; however, conclusions of whether that support influenced the outcomes could not be determined. Future comparative studies should compare nonautogenous bone sources and have longer follow-up times.
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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 (January 10, 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 Ridge, Bayesian Ridge, Support Vector Regressor (SVR), K-Nearest Neighbors Algorithm (KNN), Regression tree, Random Forest, Neural network, and Decision Tree as base learners. Ridge is used as a combiner model. Each base learner hyperparameter is tuned using a random search algorithm automatically. A random hyperparameter search optimization algorithm selects the hyperparameter and adjusts it for overfitting and underfitting. The base models are tuned to minimize bias and variance. The performances of the models are evaluated using standard error measures such as Mean Squared Error (MSE), Sum of Squared Error (SSE), and Normalized Root Mean Square Error (NRMSE). The proposed ensemble model is compared with existing models using a benchmark dataset. The Iyer,and Lee, and Musa datasets are used for the experiment. The dataset is scaled using standard methods like logarithmic scaling, lagging, and linear interpolation. The results of the statistical comparison show better performance by our proposed model as compared to existing models.
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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 (October 16, 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 combines the logF-type penalty with the ridge penalty. The results indicate that compared with other penalty methods, lFRE can not only effectively remove bias from predicted probabilities but also provide the minimum mean squared prediction error. Aside from that, a real dataset is also employed to test the performance of the lFRE algorithm compared with several existing methods. The result shows that lFRE has strong competitiveness compared with them and can be used as an alternative algorithm in logistic regression to solve separation and multicollinearity problems.
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Anderson, Kelly J., and John H. Kalivas. "Assessment of Pareto Calibration, Stability, and Wavelength Selection." Applied Spectroscopy 57, no. 3 (March 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 are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.
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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 (May 23, 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 output power dynamic range over 50 dB. Due to the frequency-selective filter integrated in the LO chain, each clutter suppression in the LO chain exceeds 40 dBc. Up and down conversion loss of the module is better than 14 dB over the 66–67 GHz band, the measured IF input P1 dB is better than 10 dBm, and the module consumes 129 mA from a 5 V low dropout supply. A low-loss ridged waveguide ladder transition was designed (less than 0.4 dB) so that the output interface of the module is a WR12 waveguide interface, which is convenient for direct connection with an instrument with E-band (60–90 GHz) waveguide interface.
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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 (July 26, 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 dengan MOS adalah; mereduksi dimensi luaran NWP, mendapatkan variabel prediktor yang mampu menjelaskan variabilitas variabel respon, dan menentukan metode statistik yang sesuai dengan karakteristik data, sehingga dapat menggambarkan hubungan antara variabel respon dan variabel prediktor. Tujuan dari penelitian ini yaitu untuk mendapatkan pemodelan MOS yang sesuai untuk variabel respon suhu maksimum, suhu minimum, dan kelembapan udara. Metode regresi yang digunakan adalah Principal Component Regression (PCR), Partial Least Square Regression (PLSR), dan ridge regression. Selanjutnya, model MOS yang terbentuk divalidasi dengan kriteria Root Mean Square Error (RMSE) dan Percentage Improval (IM%). MOS mampu mengoreksi bias prakiraan NWP hingga lebih dari 50%. Berdasarkan RMSE terkecil pada penelitian ini, suhu maksimum lebih akurat diprakirakan menggunakan model PLSR, sementara suhu minimum dan kelembapan udara lebih akurat diprakirakan menggunakan ridge regression.Kata Kunci: cuaca, MOS, NWP.
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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 (November 29, 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 such as PubMed, Scopus, Web of Science, and Science Direct. The gray literature search was conducted in the New York Academy of Medicine Grey Literature Report. All the studies in this systematic review were randomized controlled trials (RCTs). The risk of bias was performed according to the Cochrane Handbook for Systematic Reviews of Interventions 6.2 (RevMan 6.2). Considering the inclusion and exclusion criteria, we included 17 randomized clinical trials published up to 2022 investigating the efficacy of PRF in post-exodontic bone regeneration. Based on the results of clinical studies, it can be stated that despite not being statistically significant, PRF promotes neoformation and prevents bone loss between three and four months post-extraction.
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Ralston, J. D., S. Weisser, K. Eisele, R. E. Sah, E. C. Larkins, J. Rosenzweig, J. Fleissner, and K. Bender. "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 (September 1994): 1076–79. http://dx.doi.org/10.1109/68.324673.
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Ye, F., D. Moss, J. G. Simmons, P. E. Jessop, D. Landheer, H. G. Champion, I. Templeton, and F. Chatenoud. "A four-channel ridge wave-guide quantum well wavelength division demultiplexing detector and its optimization." Canadian Journal of Physics 70, no. 10-11 (October 1, 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 wavelength separation. When operated as a three-channel device, better than −15 dB crosstalk was achieved with a 18 nm wavelength separation. For transverse magnetic polarization, better than −10 dB crosstalk was achieved with a 16 nm wavelength separation. We also present a theoretical study that leads to the optimization of the device.
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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 (August 1, 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 of the discrepancies between model and observations reveals that errors on the meso-β-scale contribute at least as much to the total model error as discrepancies on the meso-γ-scale. On the scale of single mountain ridges and valleys, the formation of precipitation maxima at valley locations is investigated, with particular emphasis on a region in which a valley receives systematically more precipitation than the adjacent mountain ridges. It is found that the downstream advection of precipitation hydrometeors generated in an orographic feeder cloud is essential for the development of valley maxima. Strong ambient winds and (due to the fall speed difference between snow/graupel and rain) a low freezing level favor a large distance of the precipitation maximum from the upstream mountain ridge. Under suitable geometrical conditions, downstream advection of hydrometeors can even lead to systematically more precipitation in the valley than over the adjacent ridges. Another mechanism capable of generating a systematic rainfall maximum at valley locations requires a freezing level between valley bottom and crest height and a mountain wave flow penetrating into the valley. Under such conditions, the increase in the fall velocity related to melting of snow or graupel into rain leads to a locally intensified fallout of hydrometeors and thus to a maximum in the precipitation rate.
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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 (November 7, 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 continental shelves and slopes. Here we test an alternative approach to minimize these risks, with u measured by a low-frequency acoustic Doppler current profiler (ADCP) moored near the seabed and ρ measured by an autonomous ocean glider holding station by the ADCP. The method is used to measure the semidiurnal internal tide radiating from the Wyville Thomson Ridge in the North Atlantic. The observed energy flux (4.2±0.2 kW m−1) compares favourably with historic observations and a previous numerical model study. Error in the energy flux calculation due to imperfect co-location of the glider and ADCP is estimated by subsampling potential density in an idealized internal tide field along pseudorandomly distributed glider paths. The error is considered acceptable (<10 %) if all the glider data are contained within a “watch circle” with a diameter smaller than 1∕8 the mode-1 horizontal wavelength of the internal tide. Energy flux is biased low because the glider samples density with a broad range of phase shifts, resulting in underestimation of vertical isopycnal displacement and available potential energy. The negative bias increases with increasing watch circle diameter. If watch circle diameter is larger than 1∕8 the mode-1 horizontal wavelength, the negative bias is more than 3 % and all realizations within the 95 % confidence interval are underestimates. Over the Wyville Thomson Ridge, where the semidiurnal mode-1 horizontal wavelength is ≈100 km and all the glider dives are within a 5 km diameter watch circle, the observed energy flux is estimated to have a negative bias of only 0.4 % and an error of less than 3 % at the 95 % confidence limit. With typical glider performance, we expect energy flux error due to imperfect co-location to be <10 % in most mid-latitude shelf slope regions.
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Rault, Claire, Alexandra Robert, Odin Marc, Niels Hovius, and Patrick Meunier. "Seismic and geologic controls on spatial clustering of landslides in three large earthquakes." Earth Surface Dynamics 7, no. 3 (September 2, 2019): 829–39. http://dx.doi.org/10.5194/esurf-7-829-2019.
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Abstract. The large, shallow earthquakes at Northridge, California (1994), Chi-Chi, Taiwan (1999), and Wenchuan, China (2008), each triggered thousands of landslides. We have determined the position of these landslides along hillslopes, normalizing for statistical bias. The landslide patterns have a co-seismic signature, with clustering at ridge crests and slope toes. A cross-check against rainfall-induced landslide inventories seems to confirm that crest clustering is specific to seismic triggering as observed in previous studies. In our three study areas, the seismic ground motion parameters and lithologic and topographic features used do not seem to exert a primary control on the observed patterns of landslide clustering. However, we show that at the scale of the epicentral area, crest and toe clustering occur in areas with specific geological features. Toe clustering of seismically induced landslides tends to occur along regional major faults. Crest clustering is concentrated at sites where the lithology along hillslopes is approximately uniform, or made of alternating soft and hard strata, and without strong overprint of geological structures. Although earthquake-induced landslides locate higher on hillslopes in a statistically significant way, geological features strongly modulate the landslide position along the hillslopes. As a result the observation of landslide clustering on topographic ridges cannot be used as a definite indicator of the topographic amplification of ground shaking.
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Halliday, D. P., D. Moss, S. Charbonneau, G. C. Aers, D. Landheer, F. Chatenoud, and D. Conn. "Ultrafast electron tunnelling in a reverse-biased, high-efficiency quantum well laser structure." Canadian Journal of Physics 70, no. 10-11 (October 1, 1992): 985–92. http://dx.doi.org/10.1139/p92-158.
Full textAbstract:
We have performed a detailed series of photoconductivity (PC) and photoluminescence (PL) measurements on a reverse-biased GaAs/AlGaAs single quantum well graded index separate confinement heterostructure laser. The PC was performed, as a function of bias, at room temperature on a high-speed ridge waveguide structure. The PL was performed at low temperatures (20, 70, and, 150 K) on a ring mesa sample as a function of bias. The measurements show that this device behaves as an extremely efficient high-speed photodetector with an internal quantum efficiency of 100% and a FWHM response time of 35 ps. The data is fitted using a simple model based on electron recombination in the quantum well or escape out of the well. The escape occurs by one of three possible routes: direct tunnelling out of the lower electron level, thermally assisted tunnelling through the upper electron level, or thermionic emission over the barrier. Each of these three terms is calculated theoretically. A comparison of theory and experiment leads us to the conclusion that the theories explaining thermal emission of carriers from a quantum well underestimate the lifetimes.
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Chhalotre, Rakesh, B. Samuel Naik, V. C. Karthik, Manoj Varma, Akarsh Singh, Balan C, and Ashish Gupta. "Evaluating Model-assisted Estimators: A Comparative Study in High-dimensional Survey Data." Journal of Scientific Research and Reports 30, no. 9 (September 5, 2024): 707–18. http://dx.doi.org/10.9734/jsrr/2024/v30i92398.
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Model-assisted estimators have gained significant attention due to their ability to efficiently utilize auxiliary information during the estimation process. These estimators rely on a working model that links the survey variable to the auxiliary variables, which is then fitted to the sample data to generate predictions. These predictions are subsequently integrated into the estimation procedures. In this study, were explores various model-assisted estimators including Generalized Regression (GREG), Ridge regression, Lasso regression, CART (Classification and Regression Tree), Random Forest, Cubist and Principal Components Regression (PCR) estimator. The analysis involved 2,000 samples of size 50 (n/N ≈ 10%) and employed a stepwise variable selection method to determine the most significant auxiliary variables, incrementally adding them to the model. The performance of these estimators was assessed using relative bias (RB), relative root mean square error (RRMSE) and relative efficiency (RE). Our findings reveal that tree-based models like CART and Random Forest and penalized regression estimators such as Ridge and Lasso display robustness with increased number of auxiliary variables. Among all the estimators, Random Forest consistently yielded the lowest RRMSE, particularly with five auxiliary variables, demonstrating superior efficiency. Conversely, the GREG estimator exhibited poor performance as the number of auxiliary variables increased. This study underscores the importance of selecting suitable model-assisted estimation procedures tailored to the data characteristics and the relationship between survey and auxiliary variables within this high-dimensional dataset.
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López-Valverde, Nansi, Bruno Macedo de Sousa, and José Antonio Blanco Rueda. "Changes of the Alveolar Bone Ridge Using Bone Mineral Grafts and Collagen Membranes after Tooth Extraction: A Systematic Review and Meta-Analysis." Bioengineering 11, no. 6 (June 3, 2024): 565. http://dx.doi.org/10.3390/bioengineering11060565.
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Background: Alveolar preservation techniques for esthetic or functional purposes, or both, are a frequently used alternative for the treatment of post-extraction sockets, the aim of which is the regeneration of the lesion and the preservation of the alveolar bone crest. Methods: Studies published in PubMed (Medline), Web of Science, Embase, and Cochrane Library databases up to January 2024 were consulted. Inclusion criteria were established as intervention studies, according to the PICOs strategy: adult subjects undergoing dental extractions (participants), with alveoli treated with bone mineral grafts and collagen membranes (intervention), compared to spontaneous healing (comparison), and observing the response to treatment in clinical and radiological measures of the alveolar bone crest (outcomes). Results: We obtained 561 results and selected 12 studies. Risk of bias was assessed using the Cochrane Risk of Bias Tool, and methodological quality was assessed using the Joanna Briggs Institute. Due to the high heterogeneity of the studies (I2 > 75%), a random-effects meta-analysis was used. Despite the trend, no statistical significance (p > 0.05) was found in the experimental groups. Conclusions: The use of bone mineral grafts in combination with resorbable collagen barriers provides greater preservation of the alveolar ridge, although more clinical studies are needed.
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Davidopoulou, Sotiria, Leonidas Batas, Panagiotis Karakostas, Dimitrios Tortopidis, Panagiotis Barmpalexis, Andreana Assimopoulou, Christos Angelopoulos, and Lazaros Tsalikis. "Multidimensional 3D-Printed Scaffolds for Ridge Preservation and Dental Implant Placement: A Systematic Review." Applied Sciences 14, no. 2 (January 20, 2024): 892. http://dx.doi.org/10.3390/app14020892.
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Background: Regenerative medicine in dentistry involves tissue engineering applications suitable for the unique oral environment. In this regard, advances in computer-aided technology have facilitated the creation of 3D scaffolds using cone beam computed tomography (CBCT). This review aimed to investigate whether 3D-printed scaffolds can be effectively used to achieve ridge preservation and/or predictable vertical and horizontal bone augmentation, ensuring successful outcomes for dental implant placement. Methods: A comprehensive search was conducted across six electronic databases (PubMed, Scopus, ScienceDirect, Google Scholar, Web of Science, Ovid) to identify relevant studies according to specific eligibility criteria, following the PRISMA guidelines. Two independent reviewers screened and selected studies, performed data extraction, and assessed the risk of bias using the Cochrane tool for randomized clinical trials and the Newcastle–Ottawa Scale for non-randomized clinical trials. Results: The initial search yielded 419 articles, which were subsequently screened to remove duplicates. After evaluating 293 articles based on title and abstract, 10 studies remained for full-text assessment. Ultimately, only three studies met all the pre-established eligibility criteria. Conclusions: The studies included in this systematic review showed that the use of multidimensional customized scaffolds appears to promote dental implant placement. Nevertheless, despite the positive reported effects, further well-designed randomized clinical trials are necessary to determine the special characteristics of the optimal 3D-customized scaffold.