Journal of the Royal Statistical Society: Series B

最新文章

• Ascent-based Monte Carlo expectation-maximization AI翻译
  作者：Caffo, BS; Jank, W; Jones, GL
  作者单位：Johns Hopkins University; University System of Maryland; University of Maryland College Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：The expectation-maximization (EM) algorithm is a popular tool for maximizing likelihood functions in the presence of missing data. Unfortunately, EM often requires the evaluation of analytically intractable and high dimensional integrals. The Monte Carlo EM (MCEM) algorithm is the natural extension of EM that employs Monte Carlo methods to estimate the relevant integrals. Typically, a very large Monte Carlo sample size is required to estimate these integrals within an acceptable tolerance when...
• Self-weighted least absolute deviation estimation for infinite variance autoregressive models AI翻译
  作者：Ling, SQ
  作者单位：Hong Kong University of Science & Technology
  摘要：How to undertake statistical inference for infinite variance autoregressive models has been a long-standing open problem. To solve this problem, we propose a self-weighted least absolute deviation estimator and show that this estimator is asymptotically normal if the density of errors and its derivative are uniformly bounded. Furthermore, a Wald test statistic is developed for the linear restriction on the parameters, and it is shown to have non-trivial local power. Simulation experiments are ...
• Smooth backfitting in practice AI翻译
  作者：Nielsen, JP; Sperlich, S
  作者单位：Universidad Carlos III de Madrid
  摘要：Compared with the classical backfitting of Buja, Hastie and Tibshirani, the smooth backfitting estimator (SBE) of Mammen, Linton and Nielsen not only provides complete asymptotic theory under weaker conditions but is also more efficient, robust and easier to calculate. However, the original paper describing the SBE method is complex and the practical as well as the theoretical advantages of the method have still neither been recognized nor accepted by the statistical community. We focus on a c...
• Bayesian classification of tumours by using gene expression data AI翻译
  作者：Mallick, BK; Ghosh, D; Ghosh, M
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Michigan System; University of Michigan; State University System of Florida; University of Florida
  摘要：Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary d...
• Local model uncertainty and incomplete-data bias AI翻译
  作者：Copas, J; Eguchi, S
  作者单位：University of Warwick; Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
  摘要：Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual interpretation: the increase in v...
• Regularization and variable selection via the elastic net AI翻译
  作者：Zou, H; Hastie, T
  作者单位：Stanford University
  摘要：We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By con...
• Model-free variable selection AI翻译
  作者：Li, LX; Cook, RD; Nachtsheim, CJ
  作者单位：University of California System; University of California Davis; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：The importance of variable selection in regression has grown in recent years as computing power has encouraged the modelling of data sets of ever-increasing size. Data mining applications in finance, marketing and bioinformatics are obvious examples. A limitation of nearly all existing variable selection methods is the need to specify the correct model before selection. When the number of predictors is large, model formulation and validation can be difficult or even infeasible. On the basis of...
• Risk-reducing shrinkage estimation for generalized linear models AI翻译
  作者：Spitzner, DJ
  作者单位：Virginia Polytechnic Institute & State University
  摘要：Empirical Bayes techniques for normal theory shrinkage estimation are extended to generalized linear models in a manner retaining the original spirit of shrinkage estimation, which is to reduce risk. The investigation identifies two classes of simple, all-purpose prior distributions, which supplement such non-informative priors as Jeffreys's prior with mechanisms for risk reduction. One new class of priors is motivated as optimizers of a core component of asymptotic risk. The methodology is ev...
• Improved unbiased estimators in adaptive cluster sampling AI翻译
  作者：Dryver, AL; Thompson, SK
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：The usual design-unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics-not necessarily minimal-are described. First, estimators that are as easy to compute as the usual design-unbiased estimators are given. Estimators obtained by conditioning on the minimal sufficient statistic which are more difficult to compute are ...
• Bayesian likelihood methods for estimating the end point of a distribution AI翻译
  作者：Hall, P; Wang, JZ
  作者单位：Western Sydney University; Australian National University
  摘要：We consider maximum likelihood methods for estimating the end point of a distribution. The likelihood function is modified by a prior distribution that is imposed on the location parameter. The prior is explicit and meaningful, and has a general form that adapts itself to different settings. Results on convergence rates and limiting distributions are given. In particular, it is shown that the limiting distribution is non-normal in non-regular cases. Parametric bootstrap techniques are suggeste...