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Academic literature on the topic 'LASSO regression models'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "LASSO regression models"

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Giurcanu, Mihai, and Brett Presnell. "Bootstrapping LASSO-type estimators in regression models." Journal of Statistical Planning and Inference 199 (March 2019): 114–25. http://dx.doi.org/10.1016/j.jspi.2018.05.007.

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Li, Bohan, and Juan Wu. "Bayesian bootstrap adaptive lasso estimators of regression models." Journal of Statistical Computation and Simulation 91, no. 8 (January 11, 2021): 1651–80. http://dx.doi.org/10.1080/00949655.2020.1865959.

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Wang, Xin, Lingchen Kong, and Liqun Wang. "Estimation of Error Variance in Regularized Regression Models via Adaptive Lasso." Mathematics 10, no. 11 (June 6, 2022): 1937. http://dx.doi.org/10.3390/math10111937.

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Estimation of error variance in a regression model is a fundamental problem in statistical modeling and inference. In high-dimensional linear models, variance estimation is a difficult problem, due to the issue of model selection. In this paper, we propose a novel approach for variance estimation that combines the reparameterization technique and the adaptive lasso, which is called the natural adaptive lasso. This method can, simultaneously, select and estimate the regression and variance parameters. Moreover, we show that the natural adaptive lasso, for regression parameters, is equivalent to the adaptive lasso. We establish the asymptotic properties of the natural adaptive lasso, for regression parameters, and derive the mean squared error bound for the variance estimator. Our theoretical results show that under appropriate regularity conditions, the natural adaptive lasso for error variance is closer to the so-called oracle estimator than some other existing methods. Finally, Monte Carlo simulations are presented, to demonstrate the superiority of the proposed method.
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Emmert-Streib, Frank, and Matthias Dehmer. "High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection." Machine Learning and Knowledge Extraction 1, no. 1 (January 14, 2019): 359–83. http://dx.doi.org/10.3390/make1010021.

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Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.
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Honda, Toshio, Ching-Kang Ing, and Wei-Ying Wu. "Adaptively weighted group Lasso for semiparametric quantile regression models." Bernoulli 25, no. 4B (November 2019): 3311–38. http://dx.doi.org/10.3150/18-bej1091.

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Ahmed, S. Ejaz, Shakhawat Hossain, and Kjell A. Doksum. "LASSO and shrinkage estimation in Weibull censored regression models." Journal of Statistical Planning and Inference 142, no. 6 (June 2012): 1273–84. http://dx.doi.org/10.1016/j.jspi.2011.12.027.

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Matsui, Hidetoshi. "Sparse group lasso for multiclass functional logistic regression models." Communications in Statistics - Simulation and Computation 48, no. 6 (February 21, 2018): 1784–97. http://dx.doi.org/10.1080/03610918.2018.1423693.

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Tian, Yuzhu, Silian Shen, Ge Lu, Manlai Tang, and Maozai Tian. "Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors." Communications in Statistics - Simulation and Computation 48, no. 3 (December 6, 2017): 777–96. http://dx.doi.org/10.1080/03610918.2017.1397166.

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Xin, Seng Jia, and Kamil Khalid. "Modelling House Price Using Ridge Regression and Lasso Regression." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 498. http://dx.doi.org/10.14419/ijet.v7i4.30.22378.

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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.
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Qiao, Xin, Yoshikazu Kobayashi, Kenichi Oda, and Katsuya Nakamura. "Improved Acoustic Emission Tomography Algorithm Based on Lasso Regression." Applied Sciences 12, no. 22 (November 20, 2022): 11800. http://dx.doi.org/10.3390/app122211800.

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This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity distribution for structure evaluation. However, the new algorithm in which the LASSO algorithm is applied to AE tomography eliminates these deficiencies and reconstructs equivalent velocity distribution with fewer event data to describe the defected range. Three numerical simulation models were studied to reveal the capacity of the proposed method, and the functional performance was verified by three different types of classical concrete damage numerical simulation models and compared to that of the conventional SIRT algorithm in the experiment. Finally, this study demonstrates that the LASSO algorithm can be applied in AE tomography, and the shadow parts are eliminated in resultant elastic velocity distributions with fewer measurement paths.
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Dissertations / Theses on the topic "LASSO regression models"

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Patnaik, Kaushik. "Adaptive learning in lasso models." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54353.

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Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by noise. We examine a scenario where a fraction of the zero co-variates are highly correlated with non-zero co-variates making sparsity recovery difficult. We propose two methods that adaptively increment the regularization parameter to prune the Lasso solution set. We prove that the algorithms achieve consistent model selection with high probability while using fewer samples than traditional Lasso. The algorithm can be extended to a broad set of L1-regularized M-estimators for linear statistical models.
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Chen, Xiaohui. "Lasso-type sparse regression and high-dimensional Gaussian graphical models." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42271.

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High-dimensional datasets, where the number of measured variables is larger than the sample size, are not uncommon in modern real-world applications such as functional Magnetic Resonance Imaging (fMRI) data. Conventional statistical signal processing tools and mathematical models could fail at handling those datasets. Therefore, developing statistically valid models and computationally efficient algorithms for high-dimensional situations are of great importance in tackling practical and scientific problems. This thesis mainly focuses on the following two issues: (1) recovery of sparse regression coefficients in linear systems; (2) estimation of high-dimensional covariance matrix and its inverse matrix, both subject to additional random noise. In the first part, we focus on the Lasso-type sparse linear regression. We propose two improved versions of the Lasso estimator when the signal-to-noise ratio is low: (i) to leverage adaptive robust loss functions; (ii) to adopt a fully Bayesian modeling framework. In solution (i), we propose a robust Lasso with convex combined loss function and study its asymptotic behaviors. We further extend the asymptotic analysis to the Huberized Lasso, which is shown to be consistent even if the noise distribution is Cauchy. In solution (ii), we propose a fully Bayesian Lasso by unifying discrete prior on model size and continuous prior on regression coefficients in a single modeling framework. Since the proposed Bayesian Lasso has variable model sizes, we propose a reversible-jump MCMC algorithm to obtain its numeric estimates. In the second part, we focus on the estimation of large covariance and precision matrices. In high-dimensional situations, the sample covariance is an inconsistent estimator. To address this concern, regularized estimation is needed. For the covariance matrix estimation, we propose a shrinkage-to-tapering estimator and show that it has attractive theoretic properties for estimating general and large covariance matrices. For the precision matrix estimation, we propose a computationally efficient algorithm that is based on the thresholding operator and Neumann series expansion. We prove that, the proposed estimator is consistent in several senses under the spectral norm. Moreover, we show that the proposed estimator is minimax in a class of precision matrices that are approximately inversely closed.
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Olaya, Bucaro Orlando. "Predicting risk of cyberbullying victimization using lasso regression." Thesis, Uppsala universitet, Statistiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.

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The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation variables, school performance variables, physical health variables, mental health variables and health risk variables such as alcohol and drug consumption. Included variables in the final model are demographic variables, mental health variables and parental mediation variables. Variables excluded in the final model includes dietary habit variables, school performance variables, physical health variables and health risk variables. The final model has an overall prediction accuracy of 88%.
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Mo, Lili. "A class of operator splitting methods for least absolute shrinkage and selection operator (LASSO) models." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1391.

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Miller, Ryan. "Marginal false discovery rate approaches to inference on penalized regression models." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6474.

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Data containing large number of variables is becoming increasingly more common and sparsity inducing penalized regression methods, such the lasso, have become a popular analysis tool for these datasets due to their ability to naturally perform variable selection. However, quantifying the importance of the variables selected by these models is a difficult task. These difficulties are compounded by the tendency for the most predictive models, for example those which were chosen using procedures like cross-validation, to include substantial amounts of noise variables with no real relationship with the outcome. To address the task of performing inference on penalized regression models, this thesis proposes false discovery rate approaches for a broad class of penalized regression models. This work includes the development of an upper bound for the number of noise variables in a model, as well as local false discovery rate approaches that quantify the likelihood of each individual selection being a false discovery. These methods are applicable to a wide range of penalties, such as the lasso, elastic net, SCAD, and MCP; a wide range of models, including linear regression, generalized linear models, and Cox proportional hazards models; and are also extended to the group regression setting under the group lasso penalty. In addition to studying these methods using numerous simulation studies, the practical utility of these methods is demonstrated using real data from several high-dimensional genome wide association studies.
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Marques, Matheus Augustus Pumputis. "Análise e comparação de alguns métodos alternativos de seleção de variáveis preditoras no modelo de regressão linear." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-23082018-210710/.

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Neste trabalho estudam-se alguns novos métodos de seleção de variáveis no contexto da regressão linear que surgiram nos últimos 15 anos, especificamente o LARS - Least Angle Regression, o NAMS - Noise Addition Model Selection, a Razão de Falsa Seleção - RFS (FSR em inglês), o LASSO Bayesiano e o Spike-and-Slab LASSO. A metodologia foi a análise e comparação dos métodos estudados e aplicações. Após esse estudo, realizam-se aplicações em bases de dados reais e um estudo de simulação, em que todos os métodos se mostraram promissores, com os métodos Bayesianos apresentando os melhores resultados.
In this work, some new variable selection methods that have appeared in the last 15 years in the context of linear regression are studied, specifically the LARS - Least Angle Regression, the NAMS - Noise Addition Model Selection, the False Selection Rate - FSR, the Bayesian LASSO and the Spike-and-Slab LASSO. The methodology was the analysis and comparison of the studied methods. After this study, applications to real data bases are made, as well as a simulation study, in which all methods are shown to be promising, with the Bayesian methods showing the best results.
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Zhai, Jing, Chiu-Hsieh Hsu, and Z. John Daye. "Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer." BIOMED CENTRAL LTD, 2017. http://hdl.handle.net/10150/622811.

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Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Methods: In this article, we propose the ridge-lasso hybrid estimator (ridle), a new penalized regression method that simultaneously estimates coefficients of mandatory covariates while allowing selection for others. The ridle provides a principled approach to mitigate effects of multicollinearity among the mandatory covariates and possible dependency between mandatory and optional variables. We provide detailed empirical and theoretical studies to evaluate our method. In addition, we develop an efficient algorithm for the ridle. Software, based on efficient Fortran code with R-language wrappers, is publicly and freely available at https://sites.google.com/site/zhongyindaye/software. Results: The ridle is useful when mandatory predictors are known to be significant due to prior knowledge or must be kept for additional analysis. Both theoretical and comprehensive simulation studies have shown that the ridle to be advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves. A microarray gene expression analysis of the histologic grades of breast cancer has identified 24 genes, in which 2 genes are selected only by the ridle among current methods and found to be associated with tumor grade. Conclusions: In this article, we proposed the ridle as a principled sparse regression method for the selection of optional variables while incorporating mandatory ones. Results suggest that the ridle is advantageous when mandatory covariates are correlated with the irrelevant optional predictors or are highly correlated among themselves.
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Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.

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In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär).
In many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
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Sawert, Marcus. "Predicting deliveries from suppliers : A comparison of predictive models." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39314.

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In the highly competitive environment that companies find themselves in today, it is key to have a well-functioning supply chain. For manufacturing companies, having a good supply chain is dependent on having a functioning production planning. The production planning tries to fulfill the demand while considering the resources available. This is complicated by the uncertainties that exist, such as the uncertainty in demand, in manufacturing and in supply. Several methods and models have been created to deal with production planning under uncertainty, but they often overlook the complexity in the supply uncertainty, by considering it as a stochastic uncertainty. To improve these models, a prediction based on earlier data regarding the supplier or item could be used to see when the delivery is likely to arrive. This study looked to compare different predictive models to see which one could best be suited for this purpose. Historic data regarding earlier deliveries was gathered from a large international manufacturing company and was preprocessed before used in the models. The target value that the models were to predict was the actual delivery time from the supplier. The data was then tested with the following four regression models in Python: Linear regression, ridge regression, Lasso and Elastic net. The results were calculated by cross-validation and presented in the form of the mean absolute error together with the standard deviation. The results showed that the Elastic net was the overall best performing model, and that the linear regression performed worst.
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Yu, Lili. "Variable selection in the general linear model for censored data." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1173279515.

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Books on the topic "LASSO regression models"

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Chan-Lau, Jorge A. Lasso Regressions and Forecasting Models in Applied Stress Testing. International Monetary Fund, 2017.

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Chan-Lau, Jorge A. Lasso Regressions and Forecasting Models in Applied Stress Testing. International Monetary Fund, 2017.

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Chan-Lau, Jorge A. Lasso Regressions and Forecasting Models in Applied Stress Testing. International Monetary Fund, 2017.

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Book chapters on the topic "LASSO regression models"

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Elements for Building Supervised Statistical Machine Learning Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 71–108. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_3.

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AbstractThis chapter gives details of the linear multiple regression model including assumptions and some pros and cons, the maximum likelihood. Gradient descendent methods are described for learning the parameters under this model. Penalized linear multiple regression is derived under Ridge and Lasso penalties, which also emphasizes the estimation of the regularization parameter of importance for its successful implementation. Examples are given for both penalties (Ridge and Lasso) and but not for penalized regression multiple regression framework for illustrating the circumstances when the penalized versions should be preferred. Finally, the fundamentals of penalized and non-penalized logistic regression are provided under a gradient descendent framework. We give examples of logistic regression. Each example comes with the corresponding R codes to facilitate their quick understanding and use.
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Yüzbaşı, Bahadır, Syed Ejaz Ahmed, Mohammad Arashi, and Mina Norouzirad. "LAD, LASSO and Related Strategies in Regression Models." In Advances in Intelligent Systems and Computing, 429–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21248-3_32.

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Bunea, Florentina. "Consistent selection via the Lasso for high dimensional approximating regression models." In Institute of Mathematical Statistics Collections, 122–37. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/074921708000000101.

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Wüthrich, Mario V., and Michael Merz. "Bayesian Methods, Regularization and Expectation-Maximization." In Springer Actuarial, 207–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_6.

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AbstractThis chapter summarizes some techniques that use Bayes’ theorem. These are classical Bayesian statistical models using, e.g., the Markov chain Monte Carlo (MCMC) method for model fitting. We discuss regularization of regression models such as ridge and LASSO regularization, which has a Bayesian interpretation, and we consider the Expectation-Maximization (EM) algorithm. The EM algorithm is a general purpose tool that can handle incomplete data settings. We illustrate this for different examples coming from mixture distributions, censored and truncated claims data.
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Roy, Sanjiban Sekhar, Dishant Mittal, Avik Basu, and Ajith Abraham. "Stock Market Forecasting Using LASSO Linear Regression Model." In Advances in Intelligent Systems and Computing, 371–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13572-4_31.

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Bárzana, Marta García, Ana Colubi, and Erricos John Kontoghiorghes. "Lasso Estimation of an Interval-Valued Multiple Regression Model." In Strengthening Links Between Data Analysis and Soft Computing, 185–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10765-3_22.

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Li, Bo, Henry L. Jiang, Hanhuan Yan, Yishan Qi, and Zhiwang Gan. "Analysis of Purchasing Power Data of Department Store Members and Design of Effective Management Model." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 788–96. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_79.

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AbstractThis paper focuses on the consumption situation and discounting strategies of members in large department stores. On this basis, reasonable strategies and suggestions for discounting activities in department stores are proposed. It needs to determine the consumption habits of members, customer value, life cycle, discount effect and other information. The mathematical model was established to calculate the activation rate of non-active members in the life cycle of members, that is, the possibility of transforming from inactive members to active members. Based on the actual sales data, the relationship model between the activation rate and shopping mall promotion was determined. Generally speaking, the higher the commodity price is, the higher the profit will be. IA regression model of activation rate and promotion activities is developed. The appraisal index of market promotion activities is established in terms of both discounts and integral. Lasso regression is used for variable screening, and the correlation between activation rate and the above indicators is studied.
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Lavanya, K., L. S. S. Reddy, and B. Eswara Reddy. "A Study of High-Dimensional Data Imputation Using Additive LASSO Regression Model." In Advances in Intelligent Systems and Computing, 19–30. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8055-5_3.

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Möttönen, Jyrki, and Mikko J. Sillanpää. "Robust Variable Selection and Coefficient Estimation in Multivariate Multiple Regression Using LAD-Lasso." In Modern Nonparametric, Robust and Multivariate Methods, 235–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22404-6_14.

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Thach, Nguyen Ngoc, Le Hoang Anh, and Hoang Nguyen Khai. "Applying Lasso Linear Regression Model in Forecasting Ho Chi Minh City’s Public Investment." In Data Science for Financial Econometrics, 245–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48853-6_17.

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Conference papers on the topic "LASSO regression models"

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Omer, Pareekhan. "Improving Prediction Accuracy of Lasso and Ridge Regression as an Alternative to LS Regression to Identify Variable Selection Problems." In 3rd International Conference of Mathematics and its Applications. Salahaddin University-Erbil, 2020. http://dx.doi.org/10.31972/ticma22.05.

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This paper introduces the Lasso and Ridge Regression methods, which are two popular regularization approaches. The method they give a penalty to the coefficients differs in both of them. L1 Regularization refers to Lasso linear regression, while L2 Regularization refers to Ridge regression. As we all know, regression models serve two main purposes: explanation and prediction of scientific phenomena. Where prediction accuracy will be optimized by balancing each of the bias and variance of predictions, while explanation will be gained by constructing interpretable regression models by variable selection. The penalized regression method, also known as Lasso regression, adds bias to the model's estimates and reduces variance to enhance prediction. Ridge regression, on the other hand, introduces a minor amount of bias in the data to get long-term predictions. In the presence of multicollinearity, both regression methods have been offered as an alternative to the least square approach (LS). Because they deal with multicollinearity, they have the appropriate properties to reduce numerical instability caused by overfitting. As a result, prediction accuracy can be improved. For this study, the Corona virus disease (Covid-19) dataset was used, which has had a significant impact on global life. Particularly in our region (Kurdistan), where life has altered dramatically and many people have succumbed to this deadly sickness. Our data is utilized to analyze the benefits of each of the two regression methods. The results show that the Lasso approach produces more accurate and dependable or reliable results in the presence of multicollinearity than Ridge and LS methods when compared in terms of accuracy of predictions by using NCSS10, EViews 12 and SPSS 25.
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Al-Mudhafar, Watheq J. "Comparison of Permeability Estimation Models Through Bayesian Model Averaging and LASSO Regression." In Abu Dhabi International Petroleum Exhibition and Conference. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177556-ms.

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Zhang, Bingwen, Jun Geng, and Lifeng Lai. "Change-point estimation in high dimensional linear regression models via sparse group Lasso." In 2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton). IEEE, 2015. http://dx.doi.org/10.1109/allerton.2015.7447090.

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Sosa, Sebastián, Priscila Rivera, Cristhian Aldana, Yesenia Saavedra, Luis Trelles, and Gustavo Mendoza. "Credit classification using regulation techniques on the Credit German database." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100994.

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The development of microfinance, as well as microcredit, has generated greater competition among financial institutions to attract customers in this business segment. For this reason, the development of credit scoring models is highly required by these financial institutions. In this sense, to ensure that no overfitting is generated in the use of prediction techniques and in case of difficulty with collinearity, it will not be possible to obtain reliable estimates and predictions through common statistical techniques such as least squares; for this reason, it is significant and necessary to apply regularized regression methods such as Ridge, Lasso and Elastic Net. The present research determined the optimal credit scoring model for the Credit German database using the Ridge, Lasso, and Elastic Net regulation techniques. This dataset was initially analyzed with the Logit model, finding that this model has an accuracy of 37.2%; on the other hand, the Lasso model presented an accuracy of 76.7%, the Ridge model of 75.6%, and the Elastic Net model of 69.2%. Finally, the Lasso model evidenced the best prediction of the credit rating of Credit German future clients, with an accuracy in the training data of 82.9% and for the test data of 76.7%, being superior to the proposed models.
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Hong Hu, D. Roqueiro, and Yang Dai. "Prioritizing predicted cis-regulatory elements for co-expressed gene sets based on Lasso regression models." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091690.

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Maya, Haroldo C., and Guilherme A. Barreto. "A GA-Based Approach for Building Regularized Sparse Polynomial Models for Wind Turbine Power Curves." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4455.

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In this paper, the classical polynomial model for wind turbines power curve estimation is revisited aiming at an automatic and parsimonious design. In this regard, using genetic algorithms we introduce a methodoloy for estimating a suitable order for the polynomial as well its relevant terms. The proposed methodology is compared with the state of the art in estimating the power curve of wind turbines, such as logistic models (with 4 and 5 parameters), artificial neural networks and weighted polynomial regression. We also show that the proposed approach performs better than the standard LASSO approach for building regularized sparse models. The results indicate that the proposed methodology consistently outperforms all the evaluated alternative methods.
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Manjunatha, Koushik A., Andrea Mack, Vivek Agarwal, David Koester, and Douglas Adams. "Diagnosis of Corrosion Processes in Nuclear Power Plants Secondary Piping Structures." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21184.

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Abstract The current aging management plans of passive structures in nuclear power plants (NPPs) are based on preventative maintenance strategies. These strategies involve periodic, manual inspection of passive structures using nondestructive examination (NDE) techniques. This manual approach is prone to errors and contributes to high operation and maintenance costs, making it cost prohibitive. To address these concerns, a transition from the current preventive maintenance strategy to a condition-based maintenance strategy is needed. The research presented in this paper develops a condition-based maintenance capability to detect corrosion in secondary piping structures in NPPs. To achieve this, a data-driven methodology is developed and validated for detecting surrogate corrosion processes in piping structures. A scaled-down experimental test bed is developed to evaluate the corrosion process in secondary piping in NPPs. The experimental test bed is instrumented with tri-axial accelerometers. The data collected under different operating conditions is processed using the Hilbert-Huang Transformation. Distributional features of phase information among the accelerometers were used as features in support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression methodologies to detect changes in the pipe condition from its baseline state. SVM classification accuracy averaged 99% for all models. LASSO classification accuracy averaged 99% for all models using the accelerometer data from the X-direction.
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Idogun, Akpevwe Kelvin, Ruth Oyanu Ujah, and Lesley Anne James. "Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208452-ms.

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Abstract Optimizing decision and design variables for Chemical EOR is imperative for sensitivity and uncertainty analysis. However, these processes involve multiple reservoir simulation runs which increase computational cost and time. Surrogate models are capable of overcoming this impediment as they are capable of mimicking the capabilities of full field three-dimensional reservoir simulation models in detail and complexity. Artificial Neural Networks (ANN) and regression-based Design of Experiments (DoE) are common methods for surrogate modelling. In this study, a comparative analysis of data-driven surrogate model performance on Recovery Factor (RF) for Surfactant-Polymer flooding is investigated with seven input variables including Kv/Kh ratio, polymer concentration in polymer drive, surfactant slug size, surfactant concentration in surfactant slug, polymer concentration in surfactant slug, polymer drive size and salinity of polymer drive. Eleven Machine learning models including Multiple Linear Regression (MLR), Ridge and Lasso regression; Support Vector Regression (SVR), ANN as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT), are applied on a dataset consisting of 202 datapoints. The results obtained indicate high model performance and accuracy for SVR, ANN and CART based ensemble techniques like Extremely Randomized Trees, Gradient Boost and XGBoost regression, with high R2 values and lowest Mean Squared Error (MSE) values for the training and test dataset. Unlike other studies on Chemical EOR surrogate modelling where sensitivity was analyzed with statistical DoE, we rank the input features using Decision Tree-based algorithms while model interpretability is achieved with Shapely Values. Results from feature ranking indicate that surfactant concentration, and slug size are the most influential parameters on the RF. Other important factors, though with less influence, are the polymer concentration in surfactant slug, polymer concentration in polymer drive and polymer drive size. The salinity of the polymer drive and the Kv/Kh ratio both have a negative effect on the RF, with a corresponding least level of significance.
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Ahmadov, Jamal. "Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208628-stu.

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Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.
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Zheng, Hao, Fugui Wang, Jiangfeng Xu, Nengfeng Zhou, Minping Qian, Ji Zhu, and Minghua Deng. "Pathway Detection Based on Hierarchical LASSO Regression Model." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5305086.

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