2008

最新文章

• Non-crossing non-parametric estimates of quantile curves AI翻译
  作者：Dette, Holger; Volgushev, Stanislav
  作者单位：Ruhr University Bochum
  摘要：Since the introduction by Koenker and Bassett, quantile regression has become increasingly important in many applications. However, many non-parametric conditional quantile estimates yield crossing quantile curves (calculated for various p is an element of (0, 1)). We propose a new non-parametric estimate of conditional quantiles that avoids this problem. The method uses an initial estimate of the conditional distribution function in the first step and solves the problem of inversion and monot...
• Non-parametric inference for clustered binary and count data when only summary information is available AI翻译
  作者：Hall, Peter; Maiti, Tapabrata
  作者单位：Iowa State University; University of Melbourne
  摘要：Data in the form of pairs (X,Y), where the response Y is a count, arise in many applications, including problems involving stratified or two-stage sampling. Such data are often analysed by using random-effects models, where the distribution of Y, conditional on X and on an unobserved random parameter Theta, is taken to be either binomial or Poisson, and the distribution of Theta is connected through a link function to a random effect. The latter is sometimes supposed to be normally distributed...
• The combination of ecological and case-control data AI翻译
  作者：Haneuse, Sebastien J. -P. A.; Wakefield, Jonathan C.
  作者单位：Group Health Cooperative; University of Washington; University of Washington Seattle
  摘要：Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. To overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias r...
• A semiparametric approach to canonical analysis AI翻译
  作者：Xia, Yingcun
  作者单位：National University of Singapore
  摘要：Classical canonical correlation analysis is one of the fundamental tools in statistics to investigate the linear association between two sets of variables. We propose a method, called semiparametric canonical analysis, to generalize canonical correlation analysis to incorporate the important non-linear association. Semiparametric canonical analysis is easy to implement and interpret. Statistical properties are proved. A consistent estimation method is developed. Selection of significant semipa...
• Fast stable direct fitting and smoothness selection for generalized additive models AI翻译
  作者：Wood, Simon N.
  作者单位：University of Bath
  摘要：Existing computationally efficient methods for penalized likelihood generalized additive model fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing 'whole model' criteria these problems disappear, but until now attempts to do this have employed finite-diffe...
• A two-stage procedure for comparing hazard rate functions AI翻译
  作者：Qiu, Peihua; Sheng, Jun
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：Comparison of two hazard rates is important in applications that are related to times to occurrence of a specific event. Conventional comparison procedures, such as the log-rank, Gehan-Wilcoxon and Peto-Peto tests, are powerful only when the two hazard rates do not cross each other. Because crossing hazard rates are common in practice, several procedures have been proposed in the literature for comparing such rates. However, most of these procedures consider only the alternative hypothesis wit...
• Clustering using objective functions and stochastic search AI翻译
  作者：Booth, James G.; Casella, George; Hobert, James P.
  作者单位：Cornell University; State University System of Florida; University of Florida
  摘要：A new approach to clustering multivariate data, based on a multilevel linear mixed model, is proposed. A key feature of the model is that observations from the same cluster are correlated, because they share cluster-specific random effects. The inclusion of cluster-specific random effects allows parsimonious departure from an assumed base model for cluster mean profiles. This departure is captured statistically via the posterior expectation, or best linear unbiased predictor. One of the parame...
• Practical filtering with sequential parameter learning AI翻译
  作者：Polson, Nicholas G.; Stroud, Jonathan R.; Mueller, Peter
  作者单位：University of Chicago; University of Pennsylvania; University of Texas System; UTMD Anderson Cancer Center; University of Texas Health Science Center Houston
  摘要：The paper develops a simulation-based approach to sequential parameter learning and filtering in general state space models. Our approach is based on approximating the target posterior by a mixture of fixed lag smoothing distributions. Parameter inference exploits a sufficient statistic structure and the methodology can be easily implemented by modifying state space smoothing algorithms. We avoid reweighting particles and hence sample degeneracy problems that plague particle filters that use s...
• Separation measures and the geometry of Bayes factor selection for classification AI翻译
  作者：Smith, Jim Q.; Anderson, Paul E.; Liverani, Silvia
  作者单位：University of Warwick
  摘要：Conjugacy assumptions are often used in Bayesian selection over a partition because they allow the otherwise unfeasibly large model space to be searched very quickly. The implications of such models can be analysed algebraically. We use the explicit forms of the associated Bayes factors to demonstrate that such methods can be unstable under common settings of the associated hyperparameters. We then prove that the regions of instability can be removed by setting the hyperparameters in an unconv...
• Generalized linear models incorporating population level information: an empirical-likelihood-based approach AI翻译
  作者：Chaudhuri, Sanjay; Handcock, Mark S.; Rendall, Michael S.
  作者单位：National University of Singapore; University of Washington; University of Washington Seattle; RAND Corporation
  摘要：In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates. Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are non-linear, making the task of maximum likelihood estimati...