The Annals of Applied Statistics

最新文章

• STRUCTURE LEARNING FOR ZERO-INFLATED COUNTS WITH AN APPLICATION TO SINGLE-CELL RNA SEQUENCING DATA AI翻译
  作者：Nguyen, Thi Kim Hue; van den Berge, Koen; Chiogna, Monica; Risso, Davide
  作者单位：University of Padua; Ghent University; University of Bologna
  摘要：The problem of estimating the structure of a graph from observed data is of growing interest in the context of high-throughput genomic data and single-cell RNA sequencing in particular. These, however, are challenging applications, since the data consist of high-dimensional counts with high variance and overabundance of zeros. Here we present a general framework for learning the structure of a graph from single-cell RNA-seq data, based on the zero-inflated negative binomial distribution. We de...
• ROBUST JOINT MODELLING OF LEFT-CENSORED LONGITUDINAL DATA AND SURVIVAL DATA WITH APPLICATION TO HIV VACCINE STUDIES AI翻译
  作者：Yu, Tingting; Wu, Lang; Qiu, Jin; Gilbert, Peter B.
  作者单位：Harvard Pilgrim Health Care; Harvard University; Harvard Medical School; University of British Columbia; Zhejiang University of Finance & Economics; University of Washington; University of Washington Seattle
  摘要：In jointly modelling longitudinal and survival data, the longitudinal data may be complex in the sense that they may contain outliers and may be left censored. Motivated from an HIV vaccine study, we propose a robust method for joint models of longitudinal and survival data, where the outliers in longi-tudinal data are addressed using a multivariate t-distribution for b-outliers and using an M-estimator for e-outliers. We also propose a computationally effi-cient method for approximate likelih...
• SEQUENTIAL SAMPLING IN PROSPECTIVE OBSERVATIONAL STUDIES AI翻译
  作者：Ryan, Mary M.; Gillen, Daniel L.
  作者单位：University of California System; University of California Irvine
  摘要：Many factors must be taken into account when designing an observa-tional study. Unlike controlled studies, observational studies cannot mitigate the effects of confounding through randomization, and such factors should be incorporated into both the study analysis and the study design. Unfortu-nately, there is often little data available on most of these factors at the design stage, rendering it infeasible to reliably postulate the impact of these fac-tors on the treatment effect estimate and p...
• DATA-DRIVEN CHIMNEY FIRE RISK PREDICTION USING MACHINE LEARNING AND POINT PROCESS TOOLS AI翻译
  作者：Lu, Changqin; Van Lieshout, Marie-colette; De Graaf, Maurits; Visscher, Paul
  作者单位：University of Twente; Thales Group
  摘要：Chimney fires constitute one of the most commonly occurring fire types. Precise prediction and prompt prevention are crucial in reducing the harm they cause. In this paper we develop a combined machine learning and statistical modelling process to predict fire risk. First, we use random forests and permutation importance techniques to identify the most informative explanatory variables. Second, we design a Poisson point process model and employ logistic regression estimation to estimate the pa...
• USING PREDICTABILITY TO IMPROVE MATCHING OF URBAN LOCATIONS IN PHILADELPHIA AI翻译
  作者：Humphrey, Colman; Gross, Ryan; Small, Dylan S.; Jensen, Shane T.
  作者单位：University of Pennsylvania
  摘要：Motivated by theories in urban planning and criminology, we use highresolution data to investigate the relationship between crime and the built environment in the City of Philadelphia. We develop a novel and flexible matching framework that uses the predictability of the treatment variable within matched pairs to empirically inform both the differential weighting of covariates in the matching as well as the selection of the number of matched pairs to create. We use this matching framework for ...
• SOCIAL DISTANCING AND COVID-19: RANDOMIZATION INFERENCE FOR A STRUCTURED DOSE-RESPONSE RELATIONSHIP AI翻译
  作者：Zhang, Bo; Heng, Siyu; Ye, Ting; Small, Dylan S.
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Washington; University of Washington Seattle
  摘要：Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme forms of social distancing, like isolation and quarantine, have costs, and it is not clear how much social distancing is needed to achieve public health effects. In this article we develop a design-based framework to test the causal null hypothesis and make inference about the dose-response relationship between reduction in social mobility and COVID-19 related public healt...
• WHEN ECOLOGICAL INDIVIDUAL HETEROGENEITY MODELS AND LARGE DATA COLLIDE: AN IMPORTANCE SAMPLING APPROACH AI翻译
  作者：King, Ruth; Sarzo, Blanca; Elvira, Victor
  作者单位：University of Edinburgh; Heriot Watt University; University of Edinburgh; University of Valencia
  摘要：We consider the challenges that arise when fitting ecological individual heterogeneity models to large data sets. In particular, we focus on dividual heterogeneity present in ecological populations within the context of capture-recapture data, although the approach is more widely applicable to more general latent variable models. Within such models the associated likelihood is expressible only as an analytically intractable integral. Common techniques for fitting such models to data include, f...
• STOCHASTIC DECLUSTERING OF EARTHQUAKES WITH THE SPATIOTEMPORAL RENEWAL ETAS MODEL AI翻译
  作者：Stindl, Tom; Chen, Feng
  摘要：Modeling and forecasting earthquakes is challenging due to the complex interplay and clustering of main-shocks and aftershocks. The epidemic-type aftershock sequence (ETAS) model represents the conditional intensity of earthquakes as the superposition of a background and aftershock rate which allows for the declustering of the earthquakes. Its success has led to the development of numerous versions of the ETAS model. Among these extensions is the renewal ETAS (RETAS) model, which has shown pro...
• CALIBRATION OF SPATIOTEMPORAL FORECASTS FROM CITIZEN SCIENCE URBAN AIR POLLUTION DATA WITH SPARSE RECURRENT NEURAL NETWORKS AI翻译
  作者：Bonas, Matthew; Castruccio, Stefano
  作者单位：University of Notre Dame
  摘要：With their continued increase in coverage and quality, data collected from personal air quality monitors has become an increasingly valuable tool to complement existing public health monitoring systems over urban areas. However, the potential of using such citizen science data for automatic early warning systems is hampered by the lack of models able to capture the high-resolution, nonlinear spatiotemporal features stemming from local emission sources such as traffic, residential heating and c...
• EVALUATING THE USE OF GENERALIZED DYNAMIC WEIGHTED ORDINARY LEAST SQUARES FOR INDIVIDUALIZED HIV TREATMENT STRATEGIES AI翻译
  作者：Dong, Larry; Moodie, Erica E. M.; Villain, Laura; Thiebaut, Rodolphe
  作者单位：McGill University; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bordeaux
  摘要：A dynamic treatment regimes (DTR) represents a statistical paradigm in precision medicine which aims to optimize patient outcomes by individualizing treatments. At its simplest, a DTR may require only a single decision to be made; this special case is called an individualized treatment rule (ITR) and is often used to maximize short-term rewards. Generalized dynamic weighted ordinary least squares (G-dWOLS), a DTR estimation method that offers theoretical advantages such as double robustness of...