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作者：Wang, Lan; Van Keilegom, Ingrid; Maidman, Adam
作者单位：University of Minnesota System; University of Minnesota Twin Cities; KU Leuven
摘要：We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analysing such data. By allowing different covariates to be relevant for modelling conditional quantile functions at different quantile levels, it provides a more complete picture of the conditional distribution of a response variable than mean regression. Existing work on penalized quantile regression has be...
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作者：Lynch, Brian; Chen, Kehui
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：This paper concerns the modelling of multi-way functional data where double or multiple indices are involved. We introduce a concept of weak separability. The weakly separable structure supports the use of factorization methods that decompose the signal into its spatial and temporal components. The analysis reveals interesting connections to the usual strongly separable covariance structure, and provides insights into tensor methods for multi-way functional data. We propose a formal test for t...
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作者：McCullagh, Peter; Polson, Nicholas G.
作者单位：University of Chicago
摘要：The main contribution of this paper is a mathematical definition of statistical sparsity, which is expressed as a limiting property of a sequence of probability distributions. The limit is characterized by an exceedance measure H and a rate parameter. > 0, both of which are unrelated to sample size. The definition encompasses all sparsity models that have been suggested in the signal-detection literature. Sparsity implies that. is small, and a sparse approximation is asymptotic in the rate par...
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作者：Tian, Xiaoying; Loftus, Joshua R.; Taylor, Jonathan E.
作者单位：New York University; Stanford University
摘要：There has been much recent work on inference after model selection in situations where the noise level is known. However, the error variance is rarely known in practice and its estimation is difficult in high-dimensional settings. In this work we propose using the square-root lasso, also known as the scaled lasso, to perform inference for selected coefficients and the noise level simultaneously. The square-root lasso has the property that the choice of a reasonable tuning parameter does not de...
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作者：Ryalen, Pal C.; Stensrud, Mats J.; Roysland, Kjetil
作者单位：University of Oslo
摘要：Time-to-event outcomes are often evaluated on the hazard scale, but interpreting hazards may be difficult. Recently in the causal inference literature concerns have been raised that hazards actually have a built-in selection bias that prevents simple causal interpretations. This is a problem even in randomized controlled trials, where hazard ratios have become a standard measure of treatment effects. Modelling on the hazard scale is nevertheless convenient, for example to adjust for covariates...
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作者：Miao, Wang; Geng, Zhi; Tchetgen, Eric J. Tchetgen
作者单位：Peking University; Peking University; Harvard University
摘要：We consider a causal effect that is confounded by an unobserved variable, but for which observed proxy variables of the confounder are available. We show that with at least two independent proxy variables satisfying a certain rank condition, the causal effect can be nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified. Our result generalizes the identification strategy of Kuroki & Pe...
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作者：Tan, Kean Ming; Wang, Zhaoran; Zhang, Tong; Liu, Han; Cook, R. Dennis
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Northwestern University; Tencent
摘要：Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates. The estimated linear combinations include all covariates, making results difficult to interpret and perhaps unnecessarily variable, particularly when the number of covariates is large. In this paper, we propose a convex formulation for fitting sp...
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作者：Wei, Susan; Kosorok, Michael R.
作者单位：University of Melbourne; University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a projection pursuit technique in survival analysis for finding lower-dimensional projections that exhibit differentiated survival outcomes. This idea is formally introduced as the change-plane Cox model, a nonregular Cox model with a change-plane in the covariate space that divides the population into two subgroups whose hazards are proportional. The proposed technique offers a potential framework for principled subgroup discovery. Estimation of the change-plane is accomplished via...
