Ridge Regression and the Elastic Net: How do they do as Finders of True Regressors and their Coefficients?

2020 ◽  
Author(s):  
Rajaram Gana
Keyword(s):  
Ridge Regression ◽  
Elastic Net

scholarly journals Pendugaan Produktivitas Bagan Perahu dengan Regresi Gulud, LASSO dan Elastic-net

2018 ◽  
Vol 2 (2) ◽  
pp. 7-14
Author(s):  
Resty Fanny ◽  
Anik Djuraidah ◽  
Aam Alamudi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Ridge Regression ◽  
Penalized Regression ◽  
Statistical Technique ◽  
Elastic Net ◽  
Least Square ◽  
Productivity Change ◽  
Independent Variables ◽  
Independent Variable

Regression analysis is a statistical technique to examine and model the relationship between dependent variable and independent variable. Multiple linear regression includes more than one independent variable. Multicollinearity in multiple linear regression occurs when the independent variables has correlations. Multicolinearity causes the estimator by ordinary least square to be unstable and produce a large variety. Multicollinearity can be overcome by the addition of penalized regression coefficient. The purpose of this research is modeling ridge regression, LASSO, and elastic-net. Data which is data of fisherman catch at Carocok Beach of Tarusan Sumatera Barat as dependent variable and amount of labor, amount of fuel, volume of fishing/waring boat, number of catches, ship size, number of boat wattage, sea experience, education and age of fisher as independent variables. The best model provided by LASSO that has a RMSEP value of validated regression model is minimum than ridge regression and elastic-net. LASSO shrinked amount of labor, amount of fuel and number of wattage equal zero. There can be influence (productivity change) that is volume of fishing/waring boat and boat size that used by fisher.

scholarly journals مقارنة بعض طرائق تقدير ٳنموذج Scheff'e الخليط

2019 ◽  
Vol 25 (110) ◽  
pp. 392
Author(s):  
دجلة ابراهيم مهدي ◽  
حلا سلمان فرحان
Keyword(s):  
Ridge Regression ◽  
Regression Method ◽  
Elastic Net ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Selection Operator

نظرا لما تعانيـه تجارب الخليط من مشكلة الارتبـاطات العالية ووجود مشكلة التعدد الخطي بين المتغيرات التوضيحية وذلك لوجود قيد الوحدة والتفاعلات بينها في النموذج مما يزيد من وجود الارتباطات بين المتغيرات النوضيحية وهذا ما يوضحه عامل تضخم التباين Variance Inflation Vector (VIF) , كذلك تم التطـرق الى استخـدام تحويل المكونات الزائفة للحـدود الدنيا (L-Pseudo component) للتقليل من الارتباطات بين مكونات الخليط .    لتقدير معالم ٳنموذج الخليط اعتمدنا في بحثنا على استخدام طرائق تقدير تعمل على زيادة التحيز وتقلل من التباين منها طريقة ٳنحدار الحرف Ridge Regression Method وطريقة تقدير (Least Absolute Shrinkage and Selection Operator) (LASSO) فضلا عن طريقة تقدير الشبكة المرنة Elastic Net , وتمثيله باستخدام المحاكاة بلغة R بمعيار المقارنة متوسط مطلق الخطأ النسبي Mean Absolute Percentage Error (MAPE).

scholarly journals نموذج Tobit Quantile Regression البيزي باستعمال elastic net المكيفة المضاعفة و انحدار الحرف المكيفة

2018 ◽  
Vol 24 (107) ◽  
pp. 521
Author(s):  
محمود مهدي حسن ◽  
هيثم حسون ماجد
Keyword(s):  
Quantile Regression ◽  
Ridge Regression ◽  
Elastic Net

          نموذج (Tobit Quantile Regression) انبثق حديثا كأداة احصائية مهمة في الكثير من التحليلات الاحصائية . وبغية تطوير عملية التقدير في هذا النموذج فقد تم في هذه الدراسة  اقتراح النموذج البيزي الهرمي بتقنية elastic net المكيفة المضاعفة والنموذج الهرمي البيزي بتقنية انحدار الحرف المكيفة. في تقنية elastic net المكيفة المضاعفة تم افتراض ان كل معلمة من معلمات الجزاء(penalty parameters λ1, λ2)  تكون مختلفة لكل معلمة من معلمات النموذج ، كذلك في تقنية انحدار الحرف المكيفة فقد تم افتراض ان معلمة الجزاءpenalization parameter (λ))) تكون ايضا مختلفة لكل معلمة من معلمات النموذج .  تم استخدام اسلوب المحاكاة في بيان كفاءة الطرق المقترحة واظهرت النتائج كفاءة هذه الطرق في التعامل مع عملية تقدير معلمات  النموذج في حالة وجود ارتباطات كبيرة بين المتغيرات التوضيحية .    هذا هو العمل الاول (حسب علم الباحث) الذي يتم فيه مناقشة تقدير واختيار المتغيرات لنموذج Tobit Quantile Regression باقتراح النموذج الهرمي البيزي في تقنية elastic net  المكيفة المضاعفة وتقنية ridge regression  المكيفة.

scholarly journals Linear Regularization-based Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Meghna Chakraborty ◽  
Shakir Mahmud ◽  
Timothy Gates ◽  
Subhrajit Sinha
Keyword(s):  
Test Data ◽  
Ridge Regression ◽  
Human Mobility ◽  
State Level ◽  
Analytical Framework ◽  
Elastic Net ◽  
Training Data ◽  
Superior Performance ◽  
Data Set ◽  
The U.S

Since the increasing spread of COVID-19 in the U.S., with currently the highest number of confirmed cases and deaths in the world, most states in the nation have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the overall impact and long-term implications of this crisis to mobility still remain uncertain. To this end, this study develops an analytical framework that determines the most significant factors impacting human mobility and travel in the U.S. during the pandemic. In particular, we use Least Absolute Shrinkage and Selection Operator (LASSO) to identify the significant variables influencing human mobility and utilize linear regularization algorithms, including Ridge, LASSO, and Elastic Net modeling techniques to model and predict human mobility and travel. State-level data were obtained from various open-access sources for the period from January 1, 2020 to June 13, 2020. The entire data set was divided into a training data-set and a test data-set and the variables selected by LASSO were used to train four different models by ordinary linear regression, Ridge regression, LASSO and Elastic Net regression algorithms, using the training data-set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that among all models, the Ridge regression provides the most superior performance with the least error, while both LASSO and Elastic Net performed better than the ordinary linear model.

scholarly journals Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7281
Author(s):  
William E. Gilbraith ◽  
J. Chance Carter ◽  
Kristl L. Adams ◽  
Karl S. Booksh ◽  
Joshua M. Ottaway
Keyword(s):  
Regression Model ◽  
Ridge Regression ◽  
Near Infrared ◽  
Edible Oils ◽  
Reference Method ◽  
Elastic Net ◽  
Spectral Information ◽  
Pls Regression ◽  
Spectral Window ◽  
Wide Range

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

scholarly journals A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes

2021 ◽  
Vol 11 (5) ◽  
pp. 2040
Author(s):  
Francisco Souza ◽  
Jérôme Mendes ◽  
Rui Araújo
Keyword(s):  
Predictive Modeling ◽  
Predictive Models ◽  
Ridge Regression ◽  
Elastic Net ◽  
Polymerization Process ◽  
Industrial Processes ◽  
Quality Prediction ◽  
Process Data ◽  
Lasso Penalty

This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were compared when trained with different numbers of samples, and in comparison to other nonlinear predictive models. The models were evaluated on real multiphase polymerization process data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples.

INVESTIGATING THE IMPACT OF MULTICOLLINEARITY ON LINEAR REGRESSION ESTIMATES.

2021 ◽  
Vol 6 (1) ◽  
pp. 698
Author(s):  
Kunle Bayo Adewoye ◽  
Ayinla Bayo Rafiu ◽  
Titilope Funmilayo Aminu ◽  
Isaac Oluyemi Onikola
Keyword(s):  
Linear Regression ◽  
Sample Size ◽  
Least Squares ◽  
Ridge Regression ◽  
Research Work ◽  
Ordinary Least Squares ◽  
Small Sample ◽  
Elastic Net ◽  
Sample Sizes ◽  
The Impact

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.

scholarly journals ANALISIS TINGKAT KEMISKINAN DI PROVINSI SUMATERA BARAT MELALUI PENDEKATAN REGRESI TERKENDALA (RIDGE REGRESSION, LASSO, DAN ELASTIC NET)

2021 ◽  
Vol 21 (1) ◽  
pp. 29-36
Author(s):  
Fitri Mudia Sari ◽  
Khairil Anwar Notodiputro ◽  
Bagus Sartono
Keyword(s):  
Ridge Regression ◽  
High Dimensional Data ◽  
Elastic Net ◽  
High Dimensional

Pandemi Covid-19 yang mulai menyerang Indonesia semenjak Maret 2020 menyebabkan krisis ekonomi dan sosial di Indonesia, termasuk Sumatera Barat. Data BPS Sumatera Barat menyebutkan bahwa jumlah penduduk miskin bertambah sebanyak 20.056, dari 344.023 orang pada Maret 2020, menjadi 364.079 pada September 2020. Masalah kemiskinan merujuk pada konsep high dimensional data yang melibatkan banyak peubah sehingga digunakan Regresi Ridge, LASSO, dan Elastic Net yang dapat mengatasi masalah multikolinieritas. Penelitian ini bertujuan untuk melihat peubah yang memiliki pengaruh yang penting terhadap tingkat kemiskinan di Sumatera Barat menggunakan model terbaik yang terpilih dari Regresi Ridge, LASSO, dan Elastic Net. Hasil penelitian menunjukkan bahwa tingkat buta huruf merupakan peubah penting yang mempengaruhi tingkat kemiskinan di Sumatera Barat dengan model terbaik yaitu Regresi Ridge.

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