2期

最新文章

• MULTISTATE CAPTURE-RECAPTURE MODELS FOR IRREGULARLY SAMPLED DATA AI翻译
  作者：Mews, Sina; Langrock, Roland; King, Ruth; Quick, Nicola
  作者单位：University of Bielefeld; University of Edinburgh; University of St Andrews
  摘要：Multistate capture-recapture data comprise individual-specific sighting histories, together with information on individuals' states related, for example, to breeding status, infection level, or geographical location. Such data are often analysed using the Arnason-Schwarz model, where transitions between states are modelled using a discrete-time Markov chain, making the model most easily applicable to regular time series. When time intervals between capture occasions are not of equal length, mo...
• SCALABLE CHANGE-POINT AND ANOMALY DETECTION IN CROSS-CORRELATED DATA WITH AN APPLICATION TO CONDITION MONITORING AI翻译
  作者：Tveten, Martin; Eckley, Idris A.; Fearnhead, Paul
  作者单位：University of Oslo; Lancaster University
  摘要：Motivated by a condition monitoring application arising from subsea engineering, we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently, we explore a computationally efficient approximation of the maximum likelihood solution to the resulting modelling framework and develop a new dynamic programming algorithm for solving the resulting binary quadratic programme when the prec...
• SPARSE BLOCK SIGNAL DETECTION AND IDENTIFICATION FOR SHARED CROSS-TRAIT ASSOCIATION ANALYSIS AI翻译
  作者：Wang, Jianqiao; Wang, Wanjie; Li, Hongzhe
  作者单位：University of Pennsylvania; National University of Singapore
  摘要：Genome-wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) that are associated with complex traits. GWAS data allows us to investigate the shared genetic etiologies among different traits. However, linkage disequilibrium (LD) between the SNPs complicates the detection and identification of shared genetic effects. In this paper we model the LD by dividing the genome into LD blocks and linking the genetic variants within a block to a possible laten...
• ADAPTIVE DESIGN FOR GAUSSIAN PROCESS REGRESSION UNDER CENSORING AI翻译
  作者：Chen, Jialei; Mak, Simon; Joseph, V. Roshan; Zhang, Chuck
  作者单位：University System of Georgia; Georgia Institute of Technology; Duke University
  摘要：A key objective in engineering problems is to predict an unknown experimental surface over an input domain. In complex physical experiments this may be hampered by response censoring which results in a significant loss of information. For such problems, experimental design is paramount for maximizing predictive power using a small number of expensive experimental runs. To tackle this, we propose a novel adaptive design method, called the integrated censored mean-squared error (ICMSE) method. T...
• APPROXIMATE BAYESIAN INFERENCE FOR ANALYSIS OF SPATIOTEMPORAL FLOOD FREQUENCY DATA AI翻译
  作者：Johannesson, Arni, V; Siegert, Stefan; Huser, Raphael; Bakka, Haakon; Hrafnkelsson, Birgir
  作者单位：University of Iceland; University of Exeter; King Abdullah University of Science & Technology; University of Oslo
  摘要：Extreme floods cause casualties and widespread damage to property and vital civil infrastructure. Predictions of extreme floods, within gauged and ungauged catchments, is crucial to mitigate these disasters. In this paper a Bayesian framework is proposed for predicting extreme floods, using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are i...
• DETECTING HETEROGENEOUS TREATMENT EFFECTS WITH INSTRUMENTAL VARIABLES AND APPLICATION TO THE OREGON HEALTH INSURANCE EXPERIMENT AI翻译
  作者：Johnson, Michael; Cao, Jiongyi; Kang, Hyunseung
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Chicago
  摘要：There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand effect heterogeneity in an instrumental variables study. In this work we present a method to estimate heterogeneous causal effects using an instrumental variable with matching. The method has two parts. The first part uses subject-matter knowledge and interpretable machine-learning techniques, such as classification and...
• A BAYESIAN MODEL OF DOSE-RESPONSE FOR CANCER DRUG STUDIES AI翻译
  作者：Tansey, Wesley; Tosh, Christopher; Blei, David M.
  作者单位：Memorial Sloan Kettering Cancer Center; Columbia University
  摘要：Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian tensor filtering (BTF), a hierarchical Bayesian model for dose-response modeling in multisample, multitreatment cancer drug studies. BTF uses low-dimensional embeddings to share statistical strength between similar drugs and similar...
• MODELLED APPROXIMATIONS TO THE IDEAL FILTER WITH APPLICATION TO GDP AND ITS COMPONENTS AI翻译
  作者：Trimbur, Thomas M.; McElroy, Tucker S.
  摘要：This paper examines cyclical fluctuations in a comprehensive statistical application, focusing on U.S. macroeconomic indicators related to real gross domestic product (real GDP). While GDP is generally viewed as the most widespread measure of economic activity available, our dataset also encompasses the primary GDP components, such as investment, together with leading (and regularly analyzed) subcomponent series, like residential and inventory investment. Analysis of the cycles in these major ...