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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：Chang, Jinyuan; Delaigle, Aurore; Hall, Peter; Tang, Chengyong
作者单位：Southwestern University of Finance & Economics - China; University of Melbourne; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：Data observed at a high sampling frequency are typically assumed to be an additive composite of a relatively slow-varying continuous-time component, a latent stochastic process or smooth random function, and measurement error. Supposing that the latent component is an Ito diffusion process, we propose to estimate the measurement error density function by applying a deconvolution technique with appropriate localization. Our estimator, which does not require equally-spaced observed times, is con...
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作者：Diao, Guoqing; Zeng, Donglin; Ke, Chunlei; Ma, Haijun; Jiang, Qi; Ibrahim, Joseph G.
作者单位：George Mason University; University of North Carolina; University of North Carolina Chapel Hill; Amgen
摘要：Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression-free survival is a widely used composite endpoint, with disease progression and death as the two components. Progression-free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the endpoint event. Conve...
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作者：Chen, Yong; Huang, Jing; Ning, Yang; Liang, Kung-Yee; Lindsay, Bruce G.
作者单位：University of Pennsylvania; Cornell University; National Yang Ming Chiao Tung University
摘要：Composite likelihood has been widely used in applications. The asymptotic distribution of the composite likelihood ratio statistic at the boundary of the parameter space is a complicated mixture of weighted chi(2) distributions. In this paper we propose a conditional test with data-dependent degrees of freedom. We consider a modification of the composite likelihood which satisfies the second-order Bartlett identity. We show that the modified composite likelihood ratio statistic given the numbe...
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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：De Brabanter, K.; Cao, F.; Gijbels, I.; Opsomer, J.
作者单位：Iowa State University; KU Leuven; Colorado State University System; Colorado State University Fort Collins
摘要：Automated or data-driven bandwidth selection methods tend to break down in the presence of correlated errors. While this problem has previously been studied in the fixed design setting for kernel regression, the results were applicable only when there is knowledge about the correlation structure. This article generalizes these results to the random design setting and addresses the problem in situations where no prior knowledge about the correlation structure is available. We establish the asym...
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作者：Heard, N. A.; Rubin-Delanchy, P.
作者单位：Imperial College London; University of Bristol
摘要：Combining p-values from independent statistical tests is a popular approach to meta-analysis, particularly when the data underlying the tests are either no longer available or are difficult to combine. Numerous p-value combination methods appear in the literature, each with different statistical properties, yet often the final choice used in a meta-analysis can seem arbitrary, as if all effort has been expended in building the models that gave rise to the p-values. Birnbaum (1954) showed that ...
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作者：Kang, Jian; Reich, Brian J.; Staicu, Ana-Maria
作者单位：University of Michigan System; University of Michigan; North Carolina State University
摘要：This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational algorithm. The proposed soft-thresholded Gaussian process provides large prior support over the class of piecewise-smooth, sparse, and continuous spatially varying regression coefficient functions. In addition, under some mild regularity conditions the soft-thresholded Gaussian process prior leads to the posterior...
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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：Li, Jun
作者单位：University of California System; University of California Riverside
摘要：Interpoint distances have applications in many areas of probability and statistics. Thanks to their simplicity of computation, interpoint distance-based procedures are particularly appealing for analysing small samples of high-dimensional data. In this paper, we first study the asymptotic distribution of interpoint distances in the high-dimension, low-sample-size setting and show that it is normal under regularity conditions. We then construct a powerful test for the two-sample problem, which ...
