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Hierarchical nuclear norm penalization for multi-view data integration.

• Authors : Yi S; Department of Statistics, Oklahoma State University, Stillwater, Oklahoma, USA.; Wong RKW

Subjects: Algorithms*; Computer Simulation

• Source: Biometrics [Biometrics] 2023 Dec; Vol. 79 (4), pp. 2933-2946. Date of Electronic Publication: 2023 Jun 22.Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print-Electronic Cited

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Non-penalization and non-criminalization

• Authors : Costello, C; Ioffe, Y;

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Reduced rank regression via adaptive nuclear norm penalization

• Authors : Chen, Kun; Dong, Hongbo; Chan, Kung-Sik

Subjects: Article

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Robust Gaussian graphical modeling via l1 penalization.

• Authors : Sun H; Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. ; Li H

Subjects: Algorithms* ; Data Interpretation, Statistical* ; Models, Statistical*

• Source: Biometrics [Biometrics] 2012 Dec; Vol. 68 (4), pp. 1197-206. Date of Electronic Publication: 2012 Sep 28.Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print-Electronic Cited

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MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

• Authors : Yavorska, Olena O; Burgess, Stephen

Subjects: Mendelian randomization; causal inference; data parasite

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On the robustness of the adaptive lasso to model misspecification

• Authors : Lu, W.; Goldberg, Y.; Fine, J. P.

Subjects: Articles

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A powerful and flexible approach to the analysis of RNA sequence count data

• Authors : Zhou, Yi-Hui; Xia, Kai; Wright, Fred A.

Subjects: GENE EXPRESSION

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The Militarization of Urban Marginality: Lessons from the Brazilian Metropolis

• Authors : Wacquant, Loïc

Subjects: Original Articles

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Homogeneity pursuit and variable selection in regression models for multivariate abundance data.

• Authors : Hui FKC; Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, ACT 2601, Australia.; Maestrini L

• Source: Biometrics [Biometrics] 2024 Jan 29; Vol. 80 (1).Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium:

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Robust data integration from multiple external sources for generalized linear models with binary outcomes.

• Authors : Choi K; Department of Statistics and Data Science, Cornell University, Ithaca, NY 14853, United States.; Taylor JMG

Subjects: Models, Statistical*; Male ; Humans

• Source: Biometrics [Biometrics] 2024 Jan 29; Vol. 80 (1).Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium:

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