Journal of the American Statistical Association

最新文章

• Bayesian Calibration of Microsimulation Models AI翻译
  作者：Rutter, Carolyn M.; Miglioretti, Diana L.; Savarino, James E.
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model developm...
• Log-Linear Models for Gene Association AI翻译
  作者：Hu, Jianhua; Joshi, Adarsh; Johnson, Valen E.
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Texas A&M University System; Texas A&M University College Station
  摘要：We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens' sampling distr...
• A Statistical Framework to Infer Functional Gene Relationships From Biologically Interrelated Microarray Experiments AI翻译
  作者：Teng, Siew Leng; Huang, Haiyan
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
  摘要：A major task in understanding biological processes is to elucidate the relationships between genes involved in the underlying biological pathways. Microarray data from all increasing number of biologically interrelated experiments now allows for more complete portrayals of functional gene relationships in the pathways. In current studies of gene relationships, the presence of expression dependencies attributable to the biologically interrelated experiments, however, has been widely ignored. Wh...
• On Large Margin Hierarchical Classification With Multiple Paths AI翻译
  作者：Wang, Junhui; Shen, Xiaotong; Pan, Wei
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Hierarchical classification is critical to knowledge management and exploration. as is gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In such a situation, the central issue is how to effectively utilize the interclass relationship to improve the generalization performance of flat classification ignoring such dependency. In this article, we propose a novel large margin method through constraints c...
• Shrinkage Estimation of the Varying Coefficient Model AI翻译
  作者：Wang, Hansheng; Xia, Yingcun
  作者单位：Peking University; National University of Singapore
  摘要：The varying coefficient model is a useful extension of the linear regression model. Nevertheless, how to conduct variable selection for the varying coefficient model in a computationally efficient manner is poorly understood. To solve the problem, we propose here a novel method, which combines the ideas of the local polynomial smoothing and the Least Absolute Shrinkage and Selection Operator (LASSO). The new method can do nonparametric estimation and variable selection simultaneously. With a l...
• Order Selection in Finite Mixture Models With a Nonsmooth Penalty AI翻译
  作者：Chen, Jiahua; Khalili, Abbas
  作者单位：University of British Columbia
  摘要：Order selection is a fundamental and challenging problem in the application of finite mixture models. In this article, we develop a new penalized likelihood approach. The new method, modified smoothly clipped absolute deviation (MSCAD), deviates from information-based methods such as Akaike information criterion (AIC) and Bayesian information criterion (BIC) by introducing two penalty functions that depend on the mixing proportions and the component parameters. It is consistent at estimating b...
• On the Determination of General Scientific Models With Application to Asset Pricing AI翻译
  作者：Gallant, A. Ronald; McCulloch, Robert E.
  作者单位：Duke University; University of Texas System; University of Texas Austin
  摘要：We consider a consumption-based asset pricing model that uses habit persistence to overcome the known statistical inadequacies of the classical consumption-based asset pricing model. We find that the habit model fits reasonably well and agrees with results reported in the literature if conditional heteroskedasticity is suppressed but that it does not fit nor do result,.; agree if conditional heteroskedasticity, well known to be present in financial market data, is allowed to manifest itself. W...
• Testing Dependence Among Serially Correlated Multicategory Variables AI翻译
  作者：Pesaran, M. Hashem; Timmermann, Allan
  作者单位：University of Cambridge; University of Southern California; University of California System; University of California San Diego
  摘要：The contingency table literature On tests for dependence among discrete multicategory variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist Oil the effect of serial dependency-a problem that is important in areas Such as economics, finance, medical trials, and meteorology. This article proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests allow for an arbitrar...
• Empirical Likelihood Methods Based on Characteristic Functions With Applications to Levy Processes AI翻译
  作者：Chan, Ngai Hang; Chen, Song Xi; Peng, Liang; Yu, Cindy L.
  作者单位：Chinese University of Hong Kong; Iowa State University; Peking University; University System of Georgia; Georgia Institute of Technology
  摘要：Levy processes have been receiving increasing attention in financial modeling. One distinctive feature of such models is that their characteristic functions are readily available. Inference based on characteristic functions is very useful for studying Levy processes. By incorporating the recent advances in nonparametric approaches, empirical likelihood methods based on characteristic functions are developed in this paper for parameter estimation, testing a particular parametric class including...
• Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data AI翻译
  作者：Bayarri, M. J.; Berger, James O.; Kennedy, Marc C.; Kottas, Athanasios; Paulo, Rui; Sacks, Jerry; Cafeo, John A.; Lin, Chin-Hsu; Tu, Jian
  作者单位：University of Valencia; Duke University; Food & Environment Research Agency; University of California System; University of California Santa Cruz; Universidade de Lisboa; General Motors
  摘要：The CRASH computer model simulates the effect of a vehicle colliding against different barrier types. If it accurately represents real vehicle crashworthiness, the computer model can be of great value in various aspects of vehicle design, such as the setting of timing of air bag releases. The goal of this study is to address the problem of validating the computer model for such design goals, based on utilizing computer model runs and experimental data from real crashes. This task is complicate...