2期

最新文章

• A FLEXIBLE AND PARSIMONIOUS MODELLING STRATEGY FOR CLUSTERED DATA ANALYSIS AI翻译
  作者：Huang, Tao; Pei, Youquan; You, Jinhong; Zhang, Wenyang
  作者单位：Shanghai University of Finance & Economics; Shandong University; University of Macau
  摘要：The development of an appropriate statistical modelling strategy is of paramount importance for the successful analysis of data. The trade-off between flexibility and parsimony is of vital importance in statistical modelling. In the context of clustered data analysis, it is essential to account for the inherent heterogeneity between clusters while simultaneously ensuring parsimony to mitigate the potential for complexity and to preserve the homogeneity within clusters. The objective of this pa...
• FUNCTIONAL FACTOR MODELING OF BRAIN CONNECTIVITY AI翻译
  作者：Stanley, Kyle; Lazar, Nicole; Reimherr, Mathew
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Factor analysis, which parsimoniously describes correlations between many observed variables, offers a natural framework in which to study such dependencies. However, multivariate factor models break down when applied to functional and spatiotemporal data, like fMRI. We present a factor model for discretely-observed multidimensional functional data that is well suited to the study of fun...
• A UNIFIED QUANTILE FRAMEWORK FOR NONLINEAR HETEROGENEOUS TRANSCRIPTOME-WIDE ASSOCIATIONS AI翻译
  作者：Wang, Tianying; Ionita-Laza, Iuliana; Wei, Ying
  作者单位：Colorado State University System; Colorado State University Fort Collins; Columbia University; Lund University
  摘要：Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address this limitation, we propose a novel framework, QTWAS, which integrates a quantilebased gene expression model into the TWAS model, allowing ...
• AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA AI翻译
  作者：Moon, Haeun; Du, Jin-Hong; Lei, Jing; Roeder, Kathryn
  作者单位：Seoul National University (SNU); Carnegie Mellon University
  摘要：Quantitative measurements produced by mass spectrometry proteomics experiments offer a direct way to explore the role of proteins in molecular mechanisms. However, analysis of such data is challenging due to the large proportion of missing values. A common strategy to address this issue is to utilize an imputed dataset, which often introduces systematic bias into downstream analyses if the imputation errors are ignored. In this paper we propose a statistical framework, inspired by doubly robus...
• A BAYESIAN APPROACH TO GRAPPA PARALLEL FMRI IMAGE RECONSTRUCTION INCREASES SNR AND POWER OF TASK DETECTION AI翻译
  作者：Sakitis, Chase J.; Rowe, Daniel B.
  作者单位：Marquette University
  摘要：In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Undersampling k-space reduces the acquisition time but results in aliased, or (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpola...
• A GENERAL FRAMEWORK OF BRAIN REGION DETECTION AND GENETIC VARIANTS SELECTION IN IMAGING GENETICS AI翻译
  作者：Su, Siqiang; Li, Zhenghao; Feng, Long; Li, Ting
  作者单位：University of Hong Kong; Hong Kong Polytechnic University
  摘要：Imaging genetics is a growing field that employs structural or functional neuroimaging techniques to study individuals with genetic risk variants potentially linked to specific illnesses. This area presents considerable challenges to statisticians due to the heterogeneous information and different data forms it involves. In addition, both imaging and genetic data are typically high-dimensional, creating a big data squared problem, Moreover, brain imaging data contains extensive spatial informa...
• HOLDOUT SETS FOR SAFE PREDICTIVE MODEL UPDATING AI翻译
  作者：Haidar-Wehbe, Sami; Emerson, Samuel R.; Aslett, Louis J. M.; Liley, James
  作者单位：Durham University
  摘要：Predictive risk scores for adverse outcomes are increasingly crucial in guiding health interventions. Such scores may need to be periodically updated due to change in the distributions they model. However, directly updating risk scores used to guide intervention can lead to biased risk estimates. To address this, we propose updating using a holdout set, a subset of the population that does not receive interventions guided by the risk score. Balancing the holdout set size is essential to ensure...
• SYNTHESIZING DATA PRODUCTS, MATHEMATICAL MODELS, AND OBSERVATIONS FOR LAKE TEMPERATURE FORECASTING AI翻译
  作者：Holthuijzen, Maike f.; Gramacy, Robert b.; Carey, Cayelan c.; Higdon, David m.; Thomas, R. quinn
  作者单位：Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University; Virginia Polytechnic Institute & State University
  摘要：We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has been previously used for this purpose, but, similar to many process-based simulation models, it requires a large number of inputs (many of which are stochastic), presents challenges for uncertainty quantification (UQ), and can exhibit model bias. To address these issues, we propose a Gaussian process (GP) surrogate-bas...
• MODELING STRUCTURE AND COUNTRY-SPECIFIC HETEROGENEITY IN MISCLASSIFICATION MATRICES OF VERBAL AUTOPSY-BASED CAUSE OF DEATH CLASSIFIERS AI翻译
  作者：Pramanik, Sandipan; Zeger, Scott; Blau, Dianna; Datta, Abhirup
  作者单位：Johns Hopkins University; Centers for Disease Control & Prevention - USA
  摘要：Verbal autopsy (VA) algorithms are routinely used to determine individual-level causes of death (COD) in many low-and-middle-income countries. The individual CODs are then aggregated to derive population-level cause-specific mortality fractions (CSMF), which are essential to informing public health policies. However, VA algorithms frequently misclassify COD and introduce bias in CSMF estimates. A recent method, VA-calibration, can correct for this bias using a VA misclassification rate matrix ...
• CENSORED C-LEARNING FOR DYNAMIC TREATMENT REGIME IN COLORECTAL CANCER STUDY AI翻译
  作者：Zhan, Zishu; Liu, Zhishuai; Lin, Cunjie; Yi, Danhui; Liu, Jian; Yang, Yufei
  作者单位：Southern Medical University - China; Duke University; Renmin University of China; Renmin University of China; Beijing University of Chinese Medicine
  摘要：Dynamic treatment regimes (DTRs) represent sequential decision rules for multiple intervention stages. Each rule maps patients' covariates to optional treatments. The optimal dynamic treatment regime is the one that maximizes the mean outcome of interest if followed by the overall population. Motivated by a clinical study on the treatment of advanced colorectal cancer with traditional Chinese medicine, we propose a censored C-learning (CC-learning) method to estimate the DTR with multiple trea...