The Annals of Applied Statistics

最新文章

• BAYESIAN ADAPTIVE AND INTERPRETABLE FUNCTIONAL REGRESSION FOR EXPOSURE PROFILES AI翻译
  作者：Gao, Yunan; Kowal, Daniel r.
  作者单位：Rice University
  摘要：Pollutant exposure during gestation is a known and adverse factor for birth and health outcomes. However, the links between prenatal air pollution exposures and educational outcomes are less clear, in particular, the critical windows of susceptibility during pregnancy. Using a large cohort of students in North Carolina, we study the link between prenatal daily PM2.5 exposure and fourth end-of-grade reading scores. We develop and apply a locally adaptive and highly scalable Bayesian regression ...
• MODELS WITH OBSERVATION ERROR AND TEMPORARY EMIGRATION FOR COUNT DATA AI翻译
  作者：Ketwaroo, Fabian r.; Matechou, Eleni; Biddle, Rebecca; Tollington, Simon; DA Silva, Maria l.
  作者单位：Swiss Ornithological Institute; University of Kent; Nottingham Trent University; Universidade Federal do Para
  摘要：Count data at surveyed sites are an important monitoring tool for several species around the world. However, the raw count data are an underestimate of the size of the monitored population at any one time, as individuals can temporarily leave the site (temporary emigration, TE) and because the probability of detection of individuals, even when using the site, is typically much lower than one (observation error). In this paper we develop a novel modelling framework for estimating population siz...
• PREDICTING MILK TRAITS FROM SPECTRAL DATA USING BAYESIAN PROBABILISTIC PARTIAL LEAST SQUARES REGRESSION AI翻译
  作者：Urbas, Szymon; Lovera, Pierre; Daly, Robert; O'Riordan, Alan; Berry, Donagh; Gormley, Isobel Claire
  作者单位：University College Dublin; University College Cork; Teagasc
  摘要：High-dimensional spectral data-routinely generated in dairy production-are used to predict a range of traits in milk products. Partial least squares (PLS) regression is ubiquitously used for these prediction tasks. However, PLS regression is not typically viewed as arising from a probabilistic model, and parameter uncertainty is rarely quantified. Additionally, PLS regression does not easily lend itself to model-based modifications, coherent prediction intervals are not readily available, and ...
• A COPULA MODEL FOR MARKED POINT PROCESS WITH A TERMINAL EVENT: AN APPLICATION IN DYNAMIC PREDICTION OF INSURANCE CLAIMS AI翻译
  作者：Yang, Lu; Shi, Peng; Huang, Shimeng
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
  摘要：Accurate prediction of an insurer's outstanding liabilities is crucial for maintaining the financial health of the insurance sector. We aim to develop a statistical model for insurers to dynamically forecast unpaid losses by leveraging the granular transaction data on individual claims. The liability cash flow from a single insurance claim is determined by an event process that describes the recurrences of payments, a payment process that generates a sequence of payment amounts, and a settleme...
• A LATENT VARIABLE APPROACH FOR MODELING RELATIONAL DATA WITH MULTIPLE RECEIVERS AI翻译
  作者：Mulder, Joris; Hoff, Peter D.
  作者单位：Tilburg University; Duke University
  摘要：Directional relational event data, such as email data, often contain unicast messages (i.e., messages of one sender toward one receiver) and multicast messages (i.e., messages of one sender toward multiple receivers). The Enron email data that is the focus in this paper consists of 31% multicast messages. Multicast messages contain important information about the roles of actors in the network, which is needed for better understanding social interaction dynamics. In this paper a multiplicative...
• ASSESSING AQUATIC TOXICITY ASSESSMENT VIA A CLUSTERED VARIANCE MODEL AI翻译
  作者：Wang, Xin; Zhang, Jing
  作者单位：California State University System; San Diego State University; University System of Ohio; Miami University
  摘要：Motivated by the need to assess consistency in the outcomes of aquatic toxicity tests conducted by different labs at different time points, we propose a clustering of variance method in linear mixed models. The proposed method, referred as CVM, is able to identify the cluster structure of the variances and estimate model parameters simultaneously. In our proposed method, a penalized approach based on pairwise penalties is proposed to identify the cluster structure. We construct an optimization...
• LATENT LEVEL CORRELATION MODELING OF MULTIVARIATE DISCRETE-VALUED FINANCIAL TIME SERIES AI翻译
  作者：Wang, Yanzhao; Liu, Haitao; Zou, Jian; Ravishanker, Nalini
  作者单位：Worcester Polytechnic Institute; Worcester Polytechnic Institute; University of Connecticut
  摘要：In high-frequency financial data, dynamic patterns of transaction counts in regular time intervals provide crucial insights into market microstructure, such as short-term trading activities and intermittent intensities of price oscillation. In this paper we propose a Bayesian hierarchical framework that incorporates correlated latent level and temporal effects to model multivariate count data during intraday transaction intervals. Built on the INLA method for implementation, our framework prov...
• SPATIO-TEMPORAL ANALYSIS OF DEPENDENT RISK WITH AN APPLICATION TO CYBERATTACKS DATA AI翻译
  作者：Kim, Shonghyun; Lim, Chae Young; Rho, Yeonwoo
  作者单位：Seoul National University (SNU); Michigan Technological University
  摘要：Cybersecurity is an important issue given the increasing risks due to cyberattacks in many areas. Cyberattacks could result in huge losses such as data breaches, failures in the control systems of infrastructures, physical damages in manufacturing industries, etc. As a result, cybersecurity-related research has grown rapidly for in-depth analysis. One main interest is to understand the correlated nature of cyberattack data. To understand such characteristics, we propose a spatio-temporal model...
• CONTINUOUS AND ATLAS-FREE ANALYSIS OF BRAIN STRUCTURAL CONNECTIVITY AI翻译
  作者：Consagra, William; Cole, Martin; Qiu, Xing; Zhang, Zhengwu
  作者单位：Harvard University; Harvard Medical School; University of Rochester; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Brain structural networks are often represented as discrete adjacency matrices with elements summarizing the connectivity between pairs of regions of interest (ROIs). These ROIs are typically determined a priori using a brain atlas. The choice of atlas is often arbitrary and can lead to a loss of important connectivity information at the sub-ROI level. This work introduces an atlasfree framework that overcomes these issues by modeling brain connectivity using smooth random functions. In partic...
• SITE OCCUPANCY AND ABUNDANCE MODELS FOR ANALYZING MULTIPLE-VISIT DETECTION/NONDETECTION DATA AI翻译
  作者：Huynh, Huu-dinh; Schofield, Matthew; Hwang, Wen-han
  作者单位：National Chung Hsing University; National Tsing Hua University
  摘要：We propose an enhanced site occupancy model for analyzing ecological detection/nondetection data obtained from multiple visits. The model distinguishes between abundance, occupancy, and detection probabilities. We allow for transient individuals through a community parameter, c, that characterizes the proportion of individuals fixed across visits. This parameter seamlessly transitions from the standard occupancy model (c = 0) to the Nmixture model (c = 1), enabling a more accurate analysis of ...