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作者：Goeva, Aleksandrina; Kolaczyk, Eric D.
作者单位：Boston University
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作者：Lysy, Martin; Pillai, Natesh S.; Hill, David B.; Forest, M. Gregory; Mellnik, John W. R.; Vasquez, Paula A.; McKinley, Scott A.
作者单位：University of Waterloo; Harvard University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of South Carolina System; University of South Carolina Columbia; Tulane University
摘要：State-of-the-art techniques in passive particle-tracking microscopy provide high-resolution path trajectories of diverse foreign particles in biological fluids. For particles on the order of 1 mu m diameter, these paths are generally inconsistent with simple Brownian motion. Yet, despite an abundance of data confirming these findings and their wide-ranging scientific implications, stochastic modeling of the complex particle motion has received comparatively little attention. Even among posited...
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作者：Yee, Thomas W.
作者单位：University of Auckland
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作者：Blei, David M.
作者单位：Columbia University; Columbia University
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作者：Chen, Guanhua; Zeng, Donglin; Kosorok, Michael R.
作者单位：Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this article, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such da...
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作者：Wang, Xinlei; Lim, Johan; Stokes, Lynne
作者单位：Southern Methodist University; Seoul National University (SNU)
摘要：This article examines the use of ranked set sampling (RSS) with cluster randomized designs (CRDs), for potential improvement in estimation and detection of treatment or intervention effects. Outcome data in cluster randomized studies typically have nested structures, where hierarchical linear models (HLMs) become a natural choke for data analysis. However, nearly all theoretical developments in RSS to date are within the structure of one-level models. Thus, implementation of RSS at one or more...
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作者：Chen, Guanhua; Zeng, Donglin; Kosorok, Michael R.
作者单位：Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
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作者：Polzehl, Joerg; Tabelow, Karsten
作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
摘要：Noise is a common issue for all magnetic resonance imaging (MRI) techniques such as diffusion MRI and obviously leads to variability of the estimates in any model describing the data. Increasing spatial resolution in MR experiments further diminishes the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from the true value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the data generating proc...
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作者：Steorts, Rebecca C.; Hall, Rob; Fienberg, Stephen E.
作者单位：Duke University; Duke University; Carnegie Mellon University; Carnegie Mellon University
摘要：We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable peo...
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作者：Utts, Jessica
作者单位：University of California System; University of California Irvine
