Journal of the American Statistical Association

最新文章

• Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models AI翻译
  作者：Zhang, Hao Helen; Cheng, Guang; Liu, Yufeng
  作者单位：North Carolina State University; University of Arizona; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Partially linear models provide a useful class of tools for modeling complex data by naturally incorporating a combination of linear and nonlinear effects within one framework. One key question in partially linear models is the choice of model structure, that is, how to decide which covariates are linear and which are nonlinear. This is a fundamental, yet largely unsolved problem for partially linear models. In practice, one often assumes that the model structure is given or known and then mak...
• An Approach to the Estimation of Chronic Air Pollution Effects Using Spatio-Temporal Information AI翻译
  作者：Greven, Sonja; Dominici, Francesca; Zeger, Scott
  作者单位：University of Munich; Harvard University; Johns Hopkins University
  摘要：There is substantial observational evidence that long-term exposure to particulate air pollution is associated with premature death in urban populations. Estimates of the magnitude of these effects derive largely from cross-sectional comparisons of adjusted mortality rates among cities with varying pollution levels. Such estimates are potentially confounded by other differences among the populations correlated with air pollution, for example, socioeconomic factors. An alternative approach is t...
• Fast Robust Model Selection in Large Datasets AI翻译
  作者：Dupuis, Debbie J.; Victoria-Feser, Maria-Pia
  作者单位：Universite de Montreal; HEC Montreal; University of Geneva; University of Geneva
  摘要：Large datasets are increasingly common in many research fields. In particular, in the linear regression context, it is often the case that a huge number of potential covariates are available to explain a response variable, and the first step of a reasonable statistical analysis is to reduce the number of covariates. This can be clone in a forward selection procedure that includes selecting the variable to enter, deciding to retain it or stop the selection, and estimating the augmented model. L...
• Tweedie's Formula and Selection Bias AI翻译
  作者：Efron, Bradley
  作者单位：Stanford University
  摘要：We suppose that the statistician observes some large number of estimates z(i), each with its own unobserved expectation parameter mu(i). The largest few of the z(i)'s are likely to substantially overestimate their corresponding mu(i)'s, this being an example of selection bias, or regression to the mean. Tweedie's formula, first reported by Robbins in 1956, offers a simple empirical Bayes approach for correcting selection bias. This article investigates its merits and limitations. In addition t...
• GLS Estimation of Dynamic Factor Models AI翻译
  作者：Breitung, Joerg; Tenhofen, Joern
  作者单位：University of Bonn; Deutsche Bundesbank
  摘要：In this article a simple two-step estimation procedure of the dynamic factor model is proposed. The estimator allows for heteroscedastic and serially correlated errors. It turns out that the feasible two-step estimator has the same limiting distribution as the generalized least squares (GLS) estimator assuming that the covariance parameters are known. In a Monte Carlo study of the small sample properties, we find that the GLS estimators may be substantially more efficient than the usual estima...
• A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population AI翻译
  作者：Antal, Erika; Tille, Yves
  作者单位：University of Neuchatel
  摘要：In complex designs, classical bootstrap methods result in a biased variance estimator when the sampling design is not taken into account. Resampled units are usually rescaled or weighted in order to achieve unbiasedness in the linear case. In the present article, we propose novel resampling methods that may be directly applied to variance estimation. These methods consist of selecting subsamples under a completely different sampling scheme from that which generated the original sample, which i...
• Inference for Quantitation Parameters in Polymerase Chain Reactions via Branching Processes With Random Effects AI翻译
  作者：Hanlon, Bret; Vidyashankar, Anand N.
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：The quantitative polymerase chain reaction (qPCR) is a widely used tool for gene quantitation and has been applied extensively in several scientific areas. The current methods used for analyzing qPCR data fail to account for multiple sources of variability present in the PCR dynamics, leading to biased estimates and incorrect inference. In this article, we introduce a branching process model with random effects to account for within-reaction and between-reaction variability in PCR experiments....
• Predictive Inference for Integrated Volatility AI翻译
  作者：Corradi, Valentina; Distaso, Walter; Swanson, Norman R.
  作者单位：University of Warwick; Imperial College London; Rutgers University System; Rutgers University New Brunswick
  摘要：Numerous volatility-based derivative products have been engineered in recent years. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this article we propose nonparametric estimators of the aforementioned quantities, based on model-free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their finite-sample properties through a Monte Carlo experiment. Finall...
• Large-Scale Correlation Screening AI翻译
  作者：Hero, Alfred; Rajaratnam, Bala
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Stanford University
  摘要：This article addresses the problem of screening for variables with high correlations in high-dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single treatment (autocorrelation screening), screening for variables with large cross-correlations over two treatments (cross-correlation screening), and screening for variables that ...
• A Statistical Framework for the Analysis of ChIP-Seq Data AI翻译
  作者：Kuan, Pei Fen; Chung, Dongjun; Pan, Guangjin; Thomson, James A.; Stewart, Ron; Keles, Suenduez
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; Chinese Academy of Sciences; Guangzhou Institute of Biomedicine & Health, CAS; Chinese Academy of Sciences; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; The Morgridge Institute for Research, Inc.
  摘要：Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources ...