Joint Estimation of Multiple Precision Matrices with Common Structures
Authors: Wonyul Lee, Yufeng Liu
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical examples demonstrate that our new estimator can perform better than several existing methods in terms of the entropy loss and Frobenius loss. An application to a glioblastoma cancer data set reveals some interesting gene networks across multiple cancer subtypes. In this section, we carry out simulation studies to assess the numerical performance of our proposed method. |
| Researcher Affiliation | Academia | Wonyul Lee EMAIL Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260, USA. Yufeng Liu EMAIL Department of Statistics and Operations Research Department of Genetics Department of Biostatistics Carolina Center for Genome Sciences University of North Carolina Chapel Hill, NC 27599-3260, USA |
| Pseudocode | No | The paper describes the steps for the ADMM algorithm mathematically but does not present them in a clearly labeled 'Pseudocode' or 'Algorithm' block. For example, 'min |y|1 s.t. |zm| lambda1, |zg| lambda2, Ay Bz = C, where (5) ... As argminy L(y, zk, uk) = argminy{|y|1 + rho/2||Ay Bzk C + uk||2 2}, the step (a) can be viewed as an L1 penalized least squares problem.' |
| Open Source Code | No | For our simulation study and the GBM data analysis, we obtain the solution of (3) using the efficient R-package fastclime, which provides a generic fast linear programming solver (Pang et al., 2014). This refers to a third-party package used by the authors, not their own implementation code being released. There is no explicit statement about releasing their code for the methodology described in this paper. |
| Open Datasets | Yes | In this section, we apply our joint method to a Glioblastoma cancer data set. The data set consists of 17814 gene expression levels of 482 GBM patients... (The Cancer Genome Atlas Research Network, 2008). |
| Dataset Splits | Yes | For each group in each model, we generate a training sample of size n = 100 from either a multivariate normal distribution N(0, Sigma (g) 0 ) or a multivariate t-distribution with the covariance matrix Sigma (g) 0 and degrees of freedom of 3 or 5. In order to select optimal tuning parameters, an independent validation set of size n = 100 is also generated from the same distribution of the training sample. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | Yes | For our simulation study and the GBM data analysis, we obtain the solution of (3) using the efficient R-package fastclime, which provides a generic fast linear programming solver (Pang et al., 2014). R package version 1.2.4. |
| Experiment Setup | Yes | In our proposed method, nu is set to be G 1/2. We also tried different values of nu such as G 1, and the results are similar thus omitted. To produce interpretable graphical models using our JEMP estimator, we set the values of the tuning parameters as lambda1 = 0.30 and lambda2 = 0.40. |