Evolutionary computing enriched ridge regression model for craniofacial reconstruction

2017 ◽  
Vol 79 (31-32) ◽  
pp. 22065-22082 ◽  
Author(s):  
Romany F. Mansour
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Evolutionary Computing ◽  
Craniofacial Reconstruction

scholarly journals Modelling House Price Using Ridge Regression and Lasso Regression

2018 ◽  
Vol 7 (4.30) ◽  
pp. 498 ◽  
Author(s):  
Seng Jia Xin ◽  
Kamil Khalid
Keyword(s):  
Multivariate Analysis ◽  
Regression Model ◽  
Ridge Regression ◽  
House Price ◽  
Lasso Regression ◽  
Price Prediction ◽  
Real Estate Sector ◽  
Finance Company ◽  
The Government ◽  
House Condition

House price prediction is important for the government, finance company, real estate sector and also the house owner.  The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of  house size, age of house, condition of house and also the location of the house.

scholarly journals Modified One-Parameter Liu Estimator for the Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Adewale F. Lukman ◽  
B. M. Golam Kibria ◽  
Kayode Ayinde ◽  
Segun L. Jegede
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Ridge Regression ◽  
Real Life ◽  
Liu Estimator ◽  
Real Life Data ◽  
Squared Prediction Error ◽  
Simulation Results ◽  
Better Than

Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results.

Ridge Regression Model with Fuzzy Data

2020 ◽  
Vol 30 (5) ◽  
pp. 373-379
Author(s):  
Jae-Won Shim ◽  
Hye-Young Jung
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Fuzzy Data

scholarly journals مقارنة طرائق تقدير معلمات أنموذج انحدار ثنائي الحدين السالب في ظل وجود مشكلة التعدد الخطي باستعمال المحاكاة

2019 ◽  
Vol 25 (110) ◽  
pp. 466
Author(s):  
سهيل نجم عبود ◽  
ايناس صلاح خورشيد
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Ridge Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Liu Estimator ◽  
Binomial Regression

ناقش هذا البحث مقدر متحيز لأنموذج انحدار ثنائي الحدين السالب (Negative Binomial Regression Model) ومعرف بالمقدر ليو(Liu Estimator)، اذ استعمل هذا المقدر لتقليل التباين والتغلب على مشكلة التعدد الخطي بين المتغيرات التوضيحية، كما تم استخدام بعض التقديرات منها مقدر انحدار الحرف (Ridge Regression) ومقدر الامكان الاعظم (Maximum Likelihood)، اذ يهدف هذا البحث الى المقارنات النظرية بين مقدر (Liu Estimator) ومقدرات الامكان الاعظم (Maximum Likelihood) وانحدار الحرف (Ridge Regression) باستخدام معيار متوسط مربعات الخطأ (MSE)، اذ يكون تباين مقدر الامكان الاعظم (MLE) متضخم في ظل وجود مشكلة التعدد الخطي بين المتغيرات التوضيحية، وتم في هذا البحث تصميم المحاكاة (مونت كارلوا) لتقييم اداء المقدرات باستخدام معيار مقارنة متوسط مربعات الخطأ (MSE)، حيث اظهرت نتائج المحاكاة اهمية مقدر ليو وتفوقها على مقدري انحدار الحرف (RR) والامكان الاعظم (MLE) عندما يكون عدد المتغيرات التوضيحية (p=5)  ولحجم العينة (n=100)، اما عندما يكون عدد المتغيرات التوضيحية (p=3) ولكافة الحجوم، وكذلك عندما (p=5) ولكافة الحجوم ماعدا حجم العينة (n=100) طريقة انحدار الحرفRR  هي الافضل.  

scholarly journals Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianjiang Qi ◽  
Di He ◽  
Dagan Yang ◽  
Mengyan Wang ◽  
Wenjun Ma ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Risk Score ◽  
Ridge Regression ◽  
Clinical Decision Making ◽  
External Validation ◽  
Assessment System ◽  
Assessment Model ◽  
Support Vector ◽  
Lasso Regression

Abstract Background The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. Methods 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. Results Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914–0.943) and 0.827 (95% CI, 0.716–0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845–0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. Conclusions Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

scholarly journals FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou’s Five-Step Rule

2019 ◽  
Vol 20 (17) ◽  
pp. 4175 ◽  
Author(s):  
Yi Zou ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo ◽  
Li Peng
Keyword(s):  
Dna Binding ◽  
Regression Model ◽  
Ridge Regression ◽  
Binding Proteins ◽  
Hybrid Methods ◽  
Multiple Kernel Learning ◽  
Dna Binding Proteins ◽  
Detection Methods ◽  
Kernel Ridge Regression ◽  
Sequence Features

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively.

Sign in / Sign up

Export Citation Format

Share Document