The Annals of Applied Statistics

最新文章

• 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...
• SEMIPARAMETRIC BAYESIAN FORECASTING OF SPATIOTEMPORAL EARTHQUAKE OCCURRENCES AI翻译
  作者：Ross, Gordon J.; Kolev, Aleksandar A.
  作者单位：University of Edinburgh
  摘要：The Epidemic Type Aftershock Sequence (ETAS) model is a self -exciting point process which is used to model and forecast the occurrence of earthquakes in a geographical region. The ETAS model assumes that the oc-currence of mainshock earthquakes follows an inhomogeneous spatial point process, with their aftershock earthquakes modelled via a separate triggering kernel. Most previous studies of the ETAS model have relied on point esti-mates of the model parameters, due to the complexity of the l...
• 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...
• SUBGROUP IDENTIFICATION AND VARIABLE SELECTION FOR TREATMENT DECISION MAKING AI翻译
  作者：Zhang, Baqun; Zhang, Min
  作者单位：Shanghai University of Finance & Economics; University of Michigan System; University of Michigan
  摘要：When treatment effect heterogeneity exists, identifying the subgroup of patients who would benefit from an active treatment relative to a control is an important question. This article focuses on subgroup identification in the presence of a large dimensional set of covariates, with the number of covariates possibly greater than the sample size. We approach this problem from the perspective of optimal treatment decision rules and propose methods that can simultaneously estimate the treatment de...
• A NOVEL FRAMEWORK TO ESTIMATE MULTIDIMENSIONAL MINIMUM EFFECTIVE DOSES USING ASYMMETRIC POSTERIOR GAIN AND ε-TAPERING AI翻译
  作者：Cheung, Ying Kuen; Chandereng, Thevaa; Diaz, Keith M.
  作者单位：Columbia University; Columbia University
  摘要：In this article we address the problem of estimating minimum effective doses in dose-finding clinical trials of multidimensional treatment. We are motivated by a behavioral intervention trial where we introduce sedentary breaks to subjects with a goal to reduce their glucose level monitored over 8 hours. Each sedentary break regimen is defined by two elements: break frequency and break duration. The trial aims to identify minimum combinations of frequency and duration that shift mean glucose, ...
• LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK AI翻译
  作者：Qian, Junyang; Tanigawa, Yosuke; Li, Ruilin; Tibshirani, Robert; Rivas, Manuel A.; Hastie, Trevor
  作者单位：Stanford University; Stanford University; Stanford University
  摘要：In high-dimensional regression problems, often a relatively small subset of the features are relevant for predicting the outcome, and methods that impose sparsity on the solution are popular. When multiple correlated outcomes are available (multitask), reduced rank regression is an effective way to borrow strength and capture latent structures that underlie the data. Our proposal is motivated by the UK Biobank population-based cohort study, where we are faced with large-scale, ultrahigh-dimens...
• 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 ...
• JOINT AND INDIVIDUAL ANALYSIS OF BREAST CANCER HISTOLOGIC IMAGES AND GENOMIC COVARIATES AI翻译
  作者：Carmichael, Iain; Calhoun, Benjamin C.; Hoadley, Katherine A.; Troester, Melissa A.; Geradts, Joseph; Couture, Heather D.; Olsson, Linnea; Perou, Charles M.; Niethammer, Marc; Hannig, Jan; Marron, J. S.
  作者单位：University of Washington; University of Washington Seattle; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; East Carolina University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：The two main approaches in the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genomics. While both histopathology and genomics are fundamental to cancer research, the connections between these fields have been relatively superficial. We bridge this gap by investigating the Carolina Breast Cancer Study through the development of an integrative, exploratory analysis framework. Our analysis gives insights-some known, some novel-that are engaging to both...