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作者：Xu, Gongjun
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Statistical latent class models are widely used in social and psychological researches, yet it is often difficult to establish the identifiability of the model parameters. In this paper, we consider the identifiability issue of a family of restricted latent class models, where the restriction structures are needed to reflect pre-specified assumptions on the related assessment. We establish the identifiability results in the strict sense and specify which types of restriction structure would gi...
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作者：Cai, T. Tony; Guo, Zijian
作者单位：University of Pennsylvania
摘要：Confidence sets play a fundamental role in statistical inference. In this paper, we consider confidence intervals for high-dimensional linear regression with random design. We first establish the convergence rates of the minimax expected length for confidence intervals in the oracle setting where the sparsity parameter is given. The focus is then on the problem of adaptation to sparsity for the construction of confidence intervals. Ideally, an adaptive confidence interval should have its lengt...
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作者：Li, Jun; Zhong, Ping-Shou
作者单位：University System of Ohio; Kent State University; Kent State University Salem; Kent State University Kent; Michigan State University
摘要：The paper considers the problem of recovering the sparse different components between two high-dimensional means of column-wise dependent random vectors. We show that dependence can be utilized to lower the identification boundary for signal recovery. Moreover, an optimal convergence rate for the marginal false nondiscovery rate (mFNR) is established under dependence. The convergence rate is faster than the optimal rate without dependence. To recover the sparse signal bearing dimensions, we pr...
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作者：Cheng, Dan; Chwartzman, Armin S.
作者单位：Texas Tech University System; Texas Tech University; University of California System; University of California San Diego
摘要：A topological multiple testing scheme is presented for detecting peaks in images under stationary ergodic Gaussian noise, where tests are performed at local maxima of the smoothed observed signals. The procedure generalizes the one-dimensional scheme of Schwartzman, Gavrilov and Adler [Ann. Statist. 39 (2011) 3290-3319] to Euclidean domains of arbitrary dimension. Two methods are developed according to two different ways of computing p-values: (i) using the exact distribution of the height of ...
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作者：Kong, Yinfei; Li, Daoji; Fan, Yingying; Lv, Jinchi
作者单位：California State University System; California State University Fullerton; State University System of Florida; University of Central Florida; University of Southern California
摘要：Feature interactions can contribute to a large proportion of variation in many prediction models. In the era of big data, the coexistence of high dimensionality in both responses and covariates poses unprecedented challenges in identifying important interactions. In this paper, we suggest a two-stage interaction identification method, called the interaction pursuit via distance correlation (IPDC), in the setting of high-dimensional multi-response interaction models that exploits feature screen...
