Journal of the American Statistical Association

最新文章

• Using Secondary outcomes to Sharpen Inference in Randomized Experiments With Noncompliance AI翻译
  作者：Mealli, Fabrizia; Pacini, Barbara
  作者单位：University of Florence; University of Pisa
  摘要：We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction assumption is violated. We show how existing large-sample bounds on intention-to-treat effects for the subpopulations of compliers, never-takers, and always-takers can be tightened by exploiting the joint distribution of the outcome of interest and a secondary outcome, for which the exclusion restriction i...
• An Integrative Bayesian Modeling Approach to Imaging Genetics AI翻译
  作者：Stingo, Francesco C.; Guindani, Michele; Vannucci, Marina; Calhoun, Vince D.
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Rice University; University of New Mexico; University of New Mexico; University of New Mexico; University of New Mexico
  摘要：In this article we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance imaging (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with ava...
• Linear Transformation Model With Parametric Covariate Transformations AI翻译
  作者：Fan, Chunpeng; Fine, Jason P.
  作者单位：Sanofi-Aventis; Sanofi USA; University of North Carolina; University of North Carolina Chapel Hill
  摘要：The traditional linear transformation model assumes a linear relationship between the transformed response and the covariates. However, in real data, this linear relationship may be violated. We propose a linear transformation model that allows parametric covariate transformations to recover the linearity. Although the proposed generalization may seem rather simple, the inferential issues are quite challenging due to loss of identifiability under the null of no effects of transformed covariate...
• In Praise of Simplicity not Mathematistry! Ten Simple Powerful Ideas for the Statistical Scientist AI翻译
  作者：Little, Roderick J.
  作者单位：University of Michigan System; University of Michigan
  摘要：Ronald Fisher was by all accounts a first-rate mathematician, but he saw himself as a scientist, not a mathematician, and he railed against what George Box called (in his Fisher lecture) mathematistry. Mathematics is the indispensable foundation of statistics, but for me the real excitement and value of our subject lies in its application to other disciplines. We should not view statistics as another branch of mathematics and favor mathematical complexity over clarifying, formulating, and solv...
• A Generalized Fellegi-Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems AI翻译
  作者：Sadinle, Mauricio; Fienberg, Stephen E.
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
  摘要：We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record systems need to be integrated for posterior analysis. Our method generalizes the Fellegi-Sunter theory for linking records from two datafiles and its modem implementations. The goal of mult...
• Generalized Residuals for General Models for Contingency Tables With Application to Item Response Theory AI翻译
  作者：Haberman, Shelby J.; Sinharay, Sandip
  作者单位：Educational Testing Service (ETS)
  摘要：Generalized residuals are a tool employed in the analysis of contingency tables to examine possible sources of model error. They have typically been applied to log-linear models and to latent-class models. A general approach to generalized residuals is developed for a very general class of models for contingency tables. To illustrate their use, generalized residuals are applied to models based on item response theory (IRT) models. Such models are commonly applied to analysis of standardized ac...
• Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter AI翻译
  作者：Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
  作者单位：University System of Georgia; Augusta University; Texas A&M University System; Texas A&M University College Station
  摘要：The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle ...
• A Nonparametric Bayesian Model for Local Clustering With Application to Proteomics AI翻译
  作者：Lee, Juhee; Mueller, Peter; Zhu, Yitan; Ji, Yuan
  作者单位：University System of Ohio; Ohio State University; University of Texas System; University of Texas Austin; NorthShore University Health System
  摘要：We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. NoB-LoC implements inference for nested clusters as posterior inference under a Bayesian model. Using protein expression data as an example, the NoB-LoC model defines a protein (column) cluster as a set of proteins that give rise to the same partition of the samples (rows). In other words, the sample partitions are nested within protein clusters. The common clustering of the samples gives meaning to...
• Bayesian Hierarchical Modeling of the HIV Evolutionary Response to Therapy AI翻译
  作者：Jensen, Shane T.; Park, Jared; Braunstein, Alexander F.; McAuliffe, Jon
  作者单位：University of Pennsylvania; University of California System; University of California Berkeley
  摘要：A major challenge for the treatment of human immunodeficiency virus (HIV) infection is the development of therapy-resistant strains. We present a statistical model that quantifies the evolution of HIV populations when exposed to particular therapies. A hierarchical Bayesian approach is used to estimate differences in rates of nucleotide changes between treatment- and control-group sequences. Each group's rates are allowed to vary spatially along the HIV genome. We employ a coalescent structure...
• A Progressive Block Empirical Likelihood Method for Time Series AI翻译
  作者：Kim, Young Min; Lahiri, Soumendra N.; Nordman, Daniel J.
  作者单位：Radiation Effects Research Foundation - Japan; North Carolina State University; Iowa State University
  摘要：This article develops a new blockwise empirical likelihood (BEL) method for stationary, weakly dependent time processes, called the progressive block empirical likelihood (PBEL). In contrast to the standard version of BEL, which uses data blocks of constant length for a given sample size and whose performance can depend crucially on the block length selection, this new approach involves a data-blocking scheme where blocks increase in length by an arithmetic progression. Consequently, no block ...