Journal of the American Statistical Association

最新文章

• Inverse Regression Estimation for Censored Data AI翻译
  作者：Nadkarni, Nivedita V.; Zhao, Yingqi; Kosorok, Michael R.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：An inverse regression methodology for assessing predictor performance in the censored data setup is developed along with inference procedures and a computational algorithm. The technique developed here allows for conditioning on the unobserved failure time along with a weighting mechanism that accounts for the censoring. The implementation is nonparametric and computationally fast. This provides an efficient methodological tool that can be used especially in cases where the usual modeling assu...
• A Regularized Hotelling's T2 Test for Pathway Analysis in Proteomic Studies AI翻译
  作者：Chen, Lin S.; Paul, Debashis; Prentice, Ross L.; Wang, Pei
  作者单位：University of Chicago; University of California System; University of California Davis; Fred Hutchinson Cancer Center
  摘要：Recent proteomic studies have identified proteins related to specific phenotypes. In addition to marginal association analysis for individual proteins, analyzing pathways (functionally related sets of proteins) may yield additional valuable insights. Identifying pathways that differ between phenotypes can be conceptualized as a multivariate hypothesis testing problem: whether the mean vector mu of a p-dimensional random vector X is mu(0). Proteins within the same biological pathway may correla...
• Semiparametric Approach to a Random Effects Quantile Regression Model AI翻译
  作者：Kim, Mi-Ok; Yang, Yunwen
  作者单位：Cincinnati Children's Hospital Medical Center; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest, whereas the random coefficients represent cluster-specific deviations in the covariate effects. We ...
• Multi-Domain Sampling With Applications to Structural Inference of Bayesian Networks AI翻译
  作者：Zhou, Qing
  作者单位：University of California System; University of California Los Angeles
  摘要：When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the mean and other unco...
• Self-Exciting Point Process Modeling of Crime AI翻译
  作者：Mohler, G. O.; Short, M. B.; Brantingham, P. J.; Schoenberg, F. P.; Tita, G. E.
  作者单位：Santa Clara University; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Irvine
  摘要：Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space time clustering is modeled in seismology by self-exciting point processes and the focus of this article is to show that these methods...
• Maximum Likelihood Estimations and EM Algorithms With Length-Biased Data AI翻译
  作者：Qin, Jing; Ning, Jing; Liu, Hao; Shen, Yu
  作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID); University of Texas System; UTMD Anderson Cancer Center; Baylor College of Medicine
  摘要：Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, and epidemiological, genetic, and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimation and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for es...
• Robust, Adaptive Functional Regression in Functional Mixed Model Framework AI翻译
  作者：Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.
  作者单位：University of Kent; University of Texas System; UTMD Anderson Cancer Center
  摘要：Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying cu...
• Predictive Macro-Finance With Dynamic Partition Models AI翻译
  作者：Zantedeschi, Daniel; Damien, Paul; Polson, Nicholas G.
  作者单位：University of Texas System; University of Texas Austin; University of Chicago
  摘要：Dynamic partition models are used to predict movements in the term structure of interest rates. This allows one to understand historic cycles in the performance of how interest rates behave, and to offer policy makers guidance regarding future expectations on their evolution. Our approach allows for a random number of possible change points in the term structure of interest rates. We use particle learning to learn about the unobserved state variables in a new class of dynamic product partition...
• Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data AI翻译
  作者：Datta, Gauri S.; Hall, Peter; Mandal, Abhyuday
  作者单位：University System of Georgia; University of Georgia; University of Melbourne
  摘要：The models used in small-area inference often involve unobservable random effects. While this can significantly improve the adaptivity and flexibility of a model, it also increases the variability of both point and interval estimators. If we could test for the existence of the random effects, and if the test were to show that they were unlikely to be present, then we would arguably not need to incorporate them into the model, and thus could significantly improve the precision of the methodolog...
• Dynamic Trees for Learning and Design AI翻译
  作者：Taddy, Matthew A.; Gramacy, Robert B.; Polson, Nicholas G.
  作者单位：University of Chicago; University of Cambridge
  摘要：Dynamic regression trees are an attractive option for automatic regression and classification with complicated response surfaces in online application settings. We create a sequential tree model whose state changes in time with the accumulation of new data, and provide particle learning algorithms that allow for the efficient online posterior filtering of tree states. A major advantage of tree regression is that it allows for the use of very simple models within each partition. The model also ...