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作者：Fernandez, Itziar; Yolanda, Larriba; Canedo, Christian; Rueda, Cris tina
作者单位：Universidad de Valladolid
摘要：The Frequency Modulated M & ouml;bius (FMM) approach is a contemporary and versatile tool for analyzing oscillatory signals. Within the functional data analysis framework, the FMM model offers an accurate alternative for comprehending multidimensional synchronized oscillatory signals, which are common in various fields including biology and medicine. This approach decomposes the signals into scaled M & ouml;bius waves formulated in terms of four parameters, interpreted as measures of location ...
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作者：Rubio, Mateo Dulce; Kennedy, Edward H.; Bacak, Valerio; Nagin, Daniel S.
作者单位：Carnegie Mellon University; Carnegie Mellon University; Rutgers University System; Rutgers University New Brunswick
摘要：The causal link between victimization and violence later in life is largely accepted but has been understudied for victimized adolescents. In this work we use the Add Health dataset, the largest nationally representative longitudinal survey of adolescents, to estimate the relationship between victimization and future offending in this population. To accomplish this, we derive a new doubly robust estimator for the average treatment effect on the treated (ATT) when the exposure and outcome are n...
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作者：Chattopadhyay, Shounak; Engel, Stephanie M.; Dunson, David
作者单位：University of California System; University of California Los Angeles; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Duke University; Duke University
摘要：There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as synergistic interaction, while chemicals that inhibit each other's effects...
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作者：Cheng, Chao; Ma, Hanteng; Zhong, Yujie; Uhlemann, Anne-Catrin; Feng, Xingdong; Hu, Jianhua
作者单位：Jiangxi University of Finance & Economics; Columbia University; Columbia University
摘要：The microbiome has been found to have a close relationship with human health. Advancements in sequencing technologies have enabled in-depth studies of microbial communities and their associations with various diseases. When analyzing microbiome data, it is common to perform compositional scale normalization to ensure statistical validity. This requires special treatment to address the unique characteristics of microbiome data. Furthermore, biomedical studies often involve repeated measurements...
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作者：Wang, Fan; Zhang, Wei; Yao, Fang
作者单位：Columbia University; Peking University
摘要：The identification of genetic signal regions in the human genome is critical for understanding the genetic architecture of complex traits and diseases. Numerous methods based on scan algorithms (i.e., QSCAN, SCANG, SCANG-STAAR) have been developed to allow dynamic window sizes in whole-genome association studies. Beyond scan algorithms, we have recently developed the binary and research (BiRS) algorithm, which is more computationally efficient than scan-based methods and exhibits superior stat...
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作者：Itlevsen, Usanne; Amborrino, Assimiliano; Ubikanec, Rene
作者单位：University of Copenhagen; University of Warwick; Johannes Kepler University Linz
摘要：In this article we propose an adapted sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm for network inference in coupled stochastic differential equations (SDEs) used for multivariate time series modeling. Our approach is motivated by neuroscience, specifically the challenge of estimating brain connectivity before and during epileptic seizures. To this end, we make four key contributions. First, we introduce a 6N-dimensional SDE to model the activity of N coupled neur...
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作者：Li, Mengbing; Stephenson, Briana; Wu, Zhenke
作者单位：University of Michigan System; University of Michigan; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Dietary patterns synthesize multiple related diet components, which can be used by nutrition researchers to examine diet-disease relationships. Latent class models (LCMs) have been used to derive dietary patterns from dietary intake assessment, where each class profile represents the probabilities of exposure to a set of diet components. However, LCM-derived dietary patterns can exhibit strong similarities, or weak separation, resulting in numerical and inferential instabilities that challenge...
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作者：Yee, Thomas W.; Frigau, Luca; Ma, Chenchen
作者单位：University of Auckland; University of Cagliari; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
摘要：Large-scale health surveys suitable for addiction studies furnish self-reported data that consequently suffer from a form of measurement error called heaping, which statisticians have been grappling with for decades. Also known as digit preference, the aberration is often characterized by spikes at multiples of 10 or 5 upon rounding. To date, methods and software for heaped (and seeped) data have been largely wanting. Identifying three generic problems for simple addiction studies, we solve th...
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作者：Drew, Lane; Kaplan, Andee; Breckheimer, Ian
作者单位：Colorado State University System; Colorado State University Fort Collins
摘要：In the information age, it has become increasingly common for data containing records about overlapping individuals to be distributed across multiple sources, making it necessary to identify which records refer to the same individual. The goal of record linkage is to estimate this unknown structure in the absence of a unique identifiable attribute. We introduce a Bayesian hierarchical record linkage model for spatial location data motivated by the estimation of individual-specific growth-size ...
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作者：Redondo, Paolo Victor; Huser, Raphael; Ombao, Hernando
作者单位：King Abdullah University of Science & Technology
摘要：Brain connectivity characterizes interactions between different regions of a brain network during resting-state or performance of a cognitive task. In studying brain signals, such as electroencephalograms (EEG), one formal approach to investigating connectivity is through an information-theoretic causal measure called transfer entropy (TE). To enhance the functionality of TE in brain signal analysis, we propose a novel methodology that captures cross-channel information transfer in the frequen...
