Relevant bibliographies by topics / Bayesian LASSO

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Academic literature on the topic 'Bayesian LASSO'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Bayesian LASSO"

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Park, Trevor, and George Casella. "The Bayesian Lasso." Journal of the American Statistical Association 103, no. 482 (June 1, 2008): 681–86. http://dx.doi.org/10.1198/016214508000000337.

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Hans, C. "Bayesian lasso regression." Biometrika 96, no. 4 (September 24, 2009): 835–45. http://dx.doi.org/10.1093/biomet/asp047.

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Leng, Chenlei, Minh-Ngoc Tran, and David Nott. "Bayesian adaptive Lasso." Annals of the Institute of Statistical Mathematics 66, no. 2 (September 3, 2013): 221–44. http://dx.doi.org/10.1007/s10463-013-0429-6.

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Kadhim Abbas, Haider. "Bayesian Lasso Tobit regression." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (August 26, 2019): 1–13. http://dx.doi.org/10.29304/jqcm.2019.11.2.553.

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In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.
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Mallick, Himel, Rahim Alhamzawi, Erina Paul, and Vladimir Svetnik. "The reciprocal Bayesian LASSO." Statistics in Medicine 40, no. 22 (June 14, 2021): 4830–49. http://dx.doi.org/10.1002/sim.9098.

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Chu, Haitao, Joseph G. Ibrahim, Zakaria S. Khondker, Weili Lin, and Hongtu Zhu. "The Bayesian covariance lasso." Statistics and Its Interface 6, no. 2 (2013): 243–59. http://dx.doi.org/10.4310/sii.2013.v6.n2.a8.

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Mallick, Himel, and Nengjun Yi. "A new Bayesian lasso." Statistics and Its Interface 7, no. 4 (2014): 571–82. http://dx.doi.org/10.4310/sii.2014.v7.n4.a12.

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Kawano, Shuichi, Ibuki Hoshina, Kaito Shimamura, and Sadanori Konishi. "PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION." Journal of the Japanese Society of Computational Statistics 28, no. 1 (2015): 67–82. http://dx.doi.org/10.5183/jjscs.1501001_220.

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Alhamzawi, Rahim, and Keming Yu. "Bayesian Lasso-mixed quantile regression." Journal of Statistical Computation and Simulation 84, no. 4 (October 12, 2012): 868–80. http://dx.doi.org/10.1080/00949655.2012.731689.

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Alhamzawi, Rahim, and Haithem Taha Mohammad Ali. "The Bayesian adaptive lasso regression." Mathematical Biosciences 303 (September 2018): 75–82. http://dx.doi.org/10.1016/j.mbs.2018.06.004.

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Dissertations / Theses on the topic "Bayesian LASSO"

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Han, Yuchen. "Bayesian Variable Selection Using Lasso." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491775118610981.

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Xing, Guan. "LASSOING MIXTURES AND BAYESIAN ROBUST ESTIMATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1164135815.

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Gao, Di. "Bayesian Lasso Models – With Application to Sports Data." Diss., North Dakota State University, 2018. https://hdl.handle.net/10365/27949.

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Several statistical models were proposed by researchers to fulfill the objective of correctly predicting the winners of sports game, for example, the generalized linear model (Magel & Unruh, 2013) and the probability self-consistent model (Shen et al., 2015). This work studied Bayesian Lasso generalized linear models. A hybrid model estimation approach of full and Empirical Bayesian was proposed. A simple and efficient method in the EM step, which does not require sample mean from the random samples, was also introduced. The expectation step was reduced to derive the theoretical expectation directly from the conditional marginal. The findings of this work suggest that future application will significantly cut down the computation load. Due to Lasso (Tibshirani, 1996)’s desired geometric property, the Lasso method provides a sharp power in selecting significant explanatory variables and has become very popular in solving big data problem in the last 20 years. This work was constructed with Lasso structure hence can also be a good fit to achieve dimension reduction. Dimension reduction is necessary when the number of observations is less than the number of parameters or when the design matrix is non-full rank. A simulation study was conducted to test the power of dimension reduction and the accuracy and variation of the estimates. For an application of the Bayesian Lasso Probit Linear Regression to live data, NCAA March Madness (Men’s Basketball Division I) was considered. In the end, the predicting bracket was used to compare with the real tournament result, and the model performance was evaluated by bracket scoring system (Shen et al., 2015).
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Joo, LiJin. "Bayesian lasso| An extension for genome-wide association study." Thesis, New York University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10243856.

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In genome-wide association study (GWAS), variable selection has been used for prioritizing candidate single-nucleotide polymorphism (SNP). Relating densely located SNPs to a complex trait, we need a method that is robust under various genetic architectures, yet is sensitive enough to detect the marginal difference between null and non-null factors. For this problem, ordinary Lasso produced too many false positives, and Bayesian Lasso by Gibbs samplers became too conservative when selection criterion was posterior credible sets. My proposals to improve Bayesian Lasso include two aspects: To use stochastic approximation, variational Bayes for increasing computational efficiency and to use a Dirichlet-Laplace prior for separating small effects from nulls better. Both a double exponential prior of Bayesian Lasso and a Dirichlet-Laplace prior have a global-local mixture representation, and variational Bayes can effectively handle the hierarchies of a model due to the mixture representation. In the analysis of simulated and real sequencing data, the proposed methods showed meaningful improvements on both efficiency and accuracy.

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Zhou, Xiaofei. "Bayesian Lasso for Detecting Rare Genetic Variants Associated with Common Diseases." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1563455460578675.

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Wang, Meng. "Family-Based Bayesian LASSO for Detecting Association of Rare Haplotypes with Common Diseases." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398896091.

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Zhang, Yiran. "Bayesian Variable Selection for High-Dimensional Data with an Ordinal Response." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1565283865507018.

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Xia, Shuang. "Detecting Rare Haplotype-Environment Interaction and Dynamic Effects of Rare Haplotypes using Logistic Bayesian LASSO." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406246686.

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Fragoso, Tiago de Miranda. "Seleção bayesiana de variáveis em modelos multiníveis da teoria de resposta ao item com aplicações em genômica." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14112014-110028/.

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As investigações sobre as bases genéticas de doenças complexas em Genômica utilizam diversos tipos de informação. Diversos sintomas são avaliados de maneira a diagnosticar a doença, os indivíduos apresentam padrões de agrupamento baseados, por exemplo no seu parentesco ou ambiente comum e uma quantidade imensa de características dos indivíduos são medidas por meio de marcadores genéticos. No presente trabalho, um modelo multiníveis da teoria de resposta ao item (TRI) é proposto de forma a integrar todas essas fontes de informação e caracterizar doenças complexas através de uma variável latente. Além disso, a quantidade de marcadores moleculares induz um problema de seleção de variáveis, para o qual uma seleção baseada nos métodos da busca estocástica e do LASSO bayesiano são propostos. Os parâmetros do modelo e a seleção de variáveis são realizados sob um paradigma bayesiano, no qual um algoritmo Monte Carlo via Cadeias de Markov é construído e implementado para a obtenção de amostras da distribuição a posteriori dos parâmetros. O mesmo é validado através de estudos de simulação, nos quais a capacidade de recuperação dos parâmetros, de escolha de variáveis e características das estimativas pontuais dos parâmetros são avaliadas em cenários similares aos dados reais. O processo de estimação apresenta uma recuperação satisfatória nos parâmetros estruturais do modelo e capacidade de selecionar covariáveis em espaços de dimensão elevada apesar de um viés considerável nas estimativas das variáveis latentes associadas ao traço latente e ao efeito aleatório. Os métodos desenvolvidos são então aplicados aos dados colhidos no estudo de associação familiar \'Corações de Baependi\', nos quais o modelo multiníveis se mostra capaz de caracterizar a síndrome metabólica, uma série de sintomas associados com o risco cardiovascular. O modelo multiníveis e a seleção de variáveis se mostram capazes de recuperar características conhecidas da doença e selecionar um marcador associado.
Recent investigations about the genetic architecture of complex diseases use diferent sources of information. Diferent symptoms are measured to obtain a diagnosis, individuals may not be independent due to kinship or common environment and their genetic makeup may be measured through a large quantity of genetic markers. In the present work, a multilevel item response theory (IRT) model is proposed that unifies all these diferent sources of information through a latent variable. Furthermore, the large ammount of molecular markers induce a variable selection problem, for which procedures based on stochastic search variable selection and the Bayesian LASSO are considered. Parameter estimation and variable selection is conducted under a Bayesian framework in which a Markov chain Monte Carlo algorithm is derived and implemented to obtain posterior distribution samples. The estimation procedure is validated through a series of simulation studies in which parameter recovery, variable selection and estimation error are evaluated in scenarios similar to the real dataset. The estimation procedure showed adequate recovery of the structural parameters and the capability to correctly nd a large number of the covariates even in high dimensional settings albeit it also produced biased estimates for the incidental latent variables. The proposed methods were then applied to the real dataset collected on the \'Corações de Baependi\' familiar association study and was able to apropriately model the metabolic syndrome, a series of symptoms associated with elevated heart failure and diabetes risk. The multilevel model produced a latent trait that could be identified with the syndrome and an associated molecular marker was found.
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Zhang, Han. "Detecting Rare Haplotype-Environmental Interaction and Nonlinear Effects of Rare Haplotypes using Bayesian LASSO on Quantitative Traits." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu149969433115895.

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Books on the topic "Bayesian LASSO"

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Lenarcic, Alan B. Two-lasso Bayesian adaption to study financial contagion. 2009.

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Book chapters on the topic "Bayesian LASSO"

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Bai, Ray, Veronika Ročková, and Edward I. George. "Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO." In Handbook of Bayesian Variable Selection, 81–108. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003089018-4.

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Noguchi, Hidehisa, Yoshikazu Ojima, and Seiichi Yasui. "Bayesian Lasso with Effect Heredity Principle." In Frontiers in Statistical Quality Control 11, 355–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12355-4_21.

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Gao, Junbin, Michael Antolovich, and Paul W. Kwan. "L1 LASSO Modeling and Its Bayesian Inference." In AI 2008: Advances in Artificial Intelligence, 318–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_31.

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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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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 171–208. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.

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AbstractThe Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.
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Wang, Mei, and Erlong Yang. "Bayesian Model Averaging for Lasso Using Regularization Path." In Lecture Notes in Electrical Engineering, 273–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27296-7_43.

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Huang, Xuan, and Yinsong Ye. "Analysis of Bayesian LASSO Using High Dimensional Data." In Advances in Intelligent Systems and Computing, 75–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2568-1_12.

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Wang, Heng, Jianxu Liu, and Songsak Sriboonchitta. "Analysis of the Determinants of CO2 Emissions: A Bayesian LASSO Approach." In Lecture Notes in Computer Science, 225–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_19.

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Frederic, Patrizio, and Michele Lalla. "Determinants of the transition to upper secondary school: differences between immigrants and Italians." In Proceedings e report, 13–18. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.04.

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The determinants of the transition from lower secondary to upper secondary school of Italian and immigrant teenagers (16-19 age range) were identified joining the European Union Statistics on Income and Living Conditions (EU-SILC) and the Italian Survey on Income and Living Conditions of Families with Immigrants in Italy (IM-SILC) for 2009. A set of individual, family, and contextual characteristics was selected through the Lasso method and a Bayesian approach to explain the choice of upper secondary schooling (yes/no). The transition from the low secondary to upper secondary school showed a complex pattern involving many variables: compared to men, women did not prove to have any differences, many components of income entered the model in a parabolic form, education level and income of parents proved to be very important, as was their occupation. The contextual factors revealed their importance: the latter included the degree of urbanisation, the South macro-region, household tenure status, the amount of optional technological equipment, and so on. Differences between Italians and immigrants disappeared when family background and parental characteristics were taken into account.
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Cruz, Jose, Wilson Mamani, Christian Romero, and Ferdinand Pineda. "Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System." In Soft Computing and its Engineering Applications, 75–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0708-0_7.

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Conference papers on the topic "Bayesian LASSO"

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Mendoza, Marcela, Sanggyun Kim, and Todd P. Coleman. "Bayesian LASSO in a distributed architecture." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418402.

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Ying Wang, Wei Ding, Kui Yu, Hao Wang, and Xindong Wu. "Crater detection using Bayesian classifiers and LASSO." In 2013 IEEE Conference Anthology. IEEE, 2013. http://dx.doi.org/10.1109/anthology.2013.6784770.

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Zhang, Shenbo, Zhengbing Yan, Ping Wu, and Zhengjiang Zhang. "Fault isolation based on Bayesian fused lasso." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243248.

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Raman, Sudhir, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, and Volker Roth. "The Bayesian group-Lasso for analyzing contingency tables." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553487.

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Huri, Naor, and Meir Feder. "Selecting the LASSO regularization parameter via Bayesian principles." In 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE). IEEE, 2016. http://dx.doi.org/10.1109/icsee.2016.7806091.

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Lu, Wenxin, Zhuliang Yu, Zhenghui Gu, Jinhong Huang, Wei Gao, and Haiyu Zhou. "Variable selection using the Lasso-Cox model with Bayesian regularization." In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2018. http://dx.doi.org/10.1109/iciea.2018.8397844.

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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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Fan, Yue, and Qinke Peng. "Inferring gene regulatory networks based on spline regression and Bayesian group lasso." In 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2016. http://dx.doi.org/10.1109/snpd.2016.7515875.

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Yanuar, Ferra, Aidinil Zetra, Arrival Rince Putri, and Yudiantri Asdi. "Bayesian LASSO Quantile Regression: An Application to the Modeling of Low Birth Weight." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290341.

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Jiao, Huiyun, Risheng Huang, Xiaorun Li, and Liaoying Zhao. "A novel Bayesian lasso model based on spatial-correlated sparsity for semisupervised hyperspectral unmixing." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128233.

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