Relevant bibliographies by topics / De-biased LASSO

Academic literature on the topic 'De-biased LASSO'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "De-biased LASSO"

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Koike, Yuta. "De-Biased Graphical Lasso for High-Frequency Data." Entropy 22, no. 4 (April 17, 2020): 456. http://dx.doi.org/10.3390/e22040456.

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This paper develops a new statistical inference theory for the precision matrix of high-frequency data in a high-dimensional setting. The focus is not only on point estimation but also on interval estimation and hypothesis testing for entries of the precision matrix. To accomplish this purpose, we establish an abstract asymptotic theory for the weighted graphical Lasso and its de-biased version without specifying the form of the initial covariance estimator. We also extend the scope of the theory to the case that a known factor structure is present in the data. The developed theory is applied to the concrete situation where we can use the realized covariance matrix as the initial covariance estimator, and we obtain a feasible asymptotic distribution theory to construct (simultaneous) confidence intervals and (multiple) testing procedures for entries of the precision matrix.
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Mitra, Ritwik, and Cun-Hui Zhang. "The benefit of group sparsity in group inference with de-biased scaled group Lasso." Electronic Journal of Statistics 10, no. 2 (2016): 1829–73. http://dx.doi.org/10.1214/16-ejs1120.

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Zhan, Ming-feng, Zong-wu Cai, Ying Fang, and Ming Lin. "Recent advances in statistical methodologies in evaluating program for high-dimensional data." Applied Mathematics-A Journal of Chinese Universities 37, no. 1 (March 2022): 131–46. http://dx.doi.org/10.1007/s11766-022-4489-3.

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AbstractThe era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.
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Honda, Toshio. "The de-biased group Lasso estimation for varying coefficient models." Annals of the Institute of Statistical Mathematics, November 9, 2019. http://dx.doi.org/10.1007/s10463-019-00740-4.

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Perera, Chamila, Haixiang Zhang, Yinan Zheng, Lifang Hou, Annie Qu, Cheng Zheng, Ke Xie, and Lei Liu. "HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data." BMC Bioinformatics 23, no. 1 (July 25, 2022). http://dx.doi.org/10.1186/s12859-022-04748-1.

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AbstractMediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150–3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849–911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.
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Zhang, Haixiang, Yinan Zheng, Lifang Hou, Cheng Zheng, and Lei Liu. "Mediation analysis for survival data with High-Dimensional mediators." Bioinformatics, August 3, 2021. http://dx.doi.org/10.1093/bioinformatics/btab564.

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Abstract Motivation Mediation analysis has become a prevalent method to identify causal pathway(s) between an independent variable and a dependent variable through intermediate variable(s). However, little work has been done when the intermediate variables (mediators) are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in a causal framework of high-dimensional Cox regression. Results We first reduce the data dimension through a mediation-based sure independence screening (SIS) method. A de-biased Lasso inference procedure is used for Cox’s regression parameters. We adopt a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. Simulation studies are conducted to demonstrate the performance of our method. We apply this approach to explore the mediation mechanisms of 379,330 DNA methylation markers between smoking and overall survival among lung cancer patients in the TCGA lung cancer cohort. Two methylation sites (cg08108679 and cg26478297) are identified as potential mediating epigenetic markers. Availability Our proposed method is available with the R package HIMA at https://cran.r-project.org/web/packages/HIMA/. Supplementary information Supplementary data are available at Bioinformatics online.
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Liu, Yan, Yuzhao Gao, Ruiling Fang, Hongyan Cao, Jian Sa, Jianrong Wang, Hongqi Liu, Tong Wang, and Yuehua Cui. "Identifying complex gene–gene interactions: a mixed kernel omnibus testing approach." Briefings in Bioinformatics, August 10, 2021. http://dx.doi.org/10.1093/bib/bbab305.

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Abstract Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene–gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene–gene interactions associated with complex traits.
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Liu, Wenhu, Qiang Wang, Jinxia Chang, Anup Bhetuwal, Nisha Bhattarai, and Xin Ni. "Circulatory Metabolomics Reveals the Association of the Metabolites With Clinical Features in the Patients With Intrahepatic Cholestasis of Pregnancy." Frontiers in Physiology 13 (July 11, 2022). http://dx.doi.org/10.3389/fphys.2022.848508.

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Objective: Intrahepatic cholestasis of pregnancy (ICP) is associated with an increased risk of adverse pregnancy to the mother and fetus. As yet, the metabolic profiles and the association of the clinical features remain obscure.Methods: Fifty-seven healthy pregnant women and 52 patients with ICP were recruited in this study. Plasma samples were collected from pregnancies who received prenatal care between 30 and 36 weeks. Untargeted metabolomics to portray the metabolic profiles were performed by LC/MS. Multivariate combined with the univariate analysis was performed to screen out differential metabolites between the ICP and control groups. A de-biased sparse partial correlation (DSPC) network analysis of differential metabolites was conducted to explore the potential mutual regulation among metabolites on the basis of de-sparsified graphical lasso modeling. The pathway analysis was carried out using MetaboAnalyst. Linear regression and Pearson correlation analysis was applied to analyze correlations of bile acid levels, metabolites, newborn weights, and pregnancy outcomes in ICP patients.Results: Conspicuous metabolic changes and choreographed metabolic profiles were disclosed: 125 annotated metabolites and 18 metabolic pathways were disturbed in ICP patients. DSPC networks indicated dense interactions among amino acids and their derivatives, bile acids, carbohydrates, and organic acids. The levels of total bile acid (TBA) were increased in ICP patients with meconium-stained amniotic fluid (MSAF) compared with those without MSAF. An abnormal tryptophan metabolism, elevated long chain saturated fatty acids and estrone sulfate levels, and a low-antioxidant capacity were relevant to increased bile acid levels. Newborn weights were significantly associated with the levels of bile acids and some metabolites of amino acids.Conclusion: Our study revealed the metabolomic profiles in circulation and the correlation of the metabolites with clinical features in ICP patients. Our data suggest that disturbances in metabolic pathways might be associated with adverse pregnancy outcomes.
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Conference papers on the topic "De-biased LASSO"

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Xiong, Haoyi, Wei Cheng, Yanjie Fu, Wenqing Hu, Jiang Bian, and Zhishan Guo. "De-biasing Covariance-Regularized Discriminant Analysis." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/401.

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Fisher's Linear Discriminant Analysis (FLD) is a well-known technique for linear classification, feature extraction and dimension reduction. The empirical FLD relies on two key estimations from the data -- the mean vector for each class and the (inverse) covariance matrix. To improve the accuracy of FLD under the High Dimension Low Sample Size (HDLSS) settings, Covariance-Regularized FLD (CRLD) has been proposed to use shrunken covariance estimators, such as Graphical Lasso, to strike a balance between biases and variances. Though CRLD could obtain better classification accuracy, it usually incurs bias and converges to the optimal result with a slower asymptotic rate. Inspired by the recent progress in de-biased Lasso, we propose a novel FLD classifier, DBLD, which improves classification accuracy of CRLD through de-biasing. Theoretical analysis shows that DBLD possesses better asymptotic properties than CRLD. We conduct experiments on both synthetic datasets and real application datasets to confirm the correctness of our theoretical analysis and demonstrate the superiority of DBLD over classical FLD, CRLD and other downstream competitors under HDLSS settings.
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