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The multi-physics coupling behaviours of HTS coils have now received much attention. In particular, the electromagnetic field, temperature field and mechanical deformation interact with each other during quench of high-field magnets. Accurate analysis of coupling behaviours becomes the key to designing magnets and quench protection. In this paper, a multi-physics coupling model is proposed based on T-A formulation with Neumann boundary conditions. It is convenient to analyse the effects of deformation and temperature on the electromagnetic field, as well as the redistribution of the current between the different layers during quench.

Multiple imputation is a widely used technique to handle missing data in large observational studies. For variable selection on multiply-imputed datasets, however, if we conduct selection on each imputed dataset separately, different sets of important variables may be obtained. MI-LASSO, one of the most popular solutions to this problem, regards the same variable across all separate imputed datasets as a group of variables and exploits Group-LASSO to yield a consistent variable selection across all the multiply-imputed datasets. In this paper, we extend the MI-LASSO model into Bayesian framework and utilize five different Bayesian MI-LASSO models to perform variable selection on multiply-imputed data. These five models consist of three shrinkage priors based and two discrete mixture prior based approaches. We conduct a simulation study investigating the practical characteristics of each model across various settings. We further demonstrate these methods via a case study using the multiply-imputed data from the University of Michigan Dioxin Exposure Study. The Python package BMIselect is hosted on Github under an Apache-2.0 license: https://github.com/zjg540066169/Bmiselect.

For many practical problems, the regression models follow the strong heredity property (also known as the marginality), which means they include parent main effects when a second-order effect is present. Existing methods rely mostly on special penalty functions or algorithms to enforce the strong heredity in variable selection. We propose a novel hierarchical standardization procedure to maintain strong heredity in variable selection. Our method is effortless to implement and is applicable to any variable selection method for any type of regression. The performance of the hierarchical standardization is comparable to that of the regular standardization. We also provide robustness checks and real data analysis to illustrate the merits of our method., Comment: 32 pages

This paper considers robust modeling of the survival time for cancer patients. Accurate prediction can be helpful for developing therapeutic and care strategies. We propose a unified Expectation-Maximization approach combined with the L1-norm penalty to perform variable selection and obtain parameter estimation simultaneously for the accelerated failure time model with right-censored survival data. Our approach can be used with general loss functions, and reduces to the well-known Buckley-James method when the squared-error loss is used without regularization. To mitigate the effects of outliers and heavy-tailed noise in the real application, we advocate the use of robust loss functions under our proposed framework. Simulation studies are conducted to evaluate the performance of the proposed approach with different loss functions, and an application to an ovarian carcinoma study is provided. Meanwhile, we extend our approach by incorporating the group structure of covariates., Comment: 21 pages, , 1 figures

Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response variable, and is able to characterize the underlying regression structure in a more comprehensive manner. On the other hand, genetic studies often involve high dimensional genomic features, and the underlying regression structure may be heterogeneous in terms of both effect sizes and sparsity. To account for the potential genetic heterogeneity, including the heterogeneous sparsity, a regularized quantile regression method is introduced. The theoretical property of the proposed method is investigated, and its performance is examined through a series of simulation studies. A real dataset is analyzed to demonstrate the application of the proposed method.