474期

最新文章

• A constructive representation of univariate skewed distributions AI翻译
  作者：Ferreira, Jose T. A. S.; Steel, Mark F. J.
  作者单位：University of Warwick
  摘要：We introduce a general perspective on the introduction of skewness into symmetric distributions. Through inverse probability integral transformations we provide a constructive representation of skewed distributions, where the skewing mechanism and the original symmetric distributions are specified separately. We study the effects of the skewing mechanism on, e.g., modality, tail behavior and the amount of skewness generated. The representation is used to introduce novel classes of skewed distr...
• Incorporating additional information to normal linear discriminant rules AI翻译
  作者：Fernandez, Miguel A.; Rueda, Cristina; Salvador, Bonifacio
  作者单位：Universidad de Valladolid
  摘要：The most useful and broadly known rule in the classical two-group linear normal discriminant analysis is Anderson's rule. In this article we propose some alternative procedures that prove useful when prior constraints on the mean vectors are known. These rules are based on new estimators of the difference of means. We prove under mild conditions that the new rules perform better when the common covariance matrix is known. Simulated experiments show that the misclassification errors are lower f...
• On δ-equivalence with the best in k-sample models AI翻译
  作者：Finner, H.; Strassburger, K.
  作者单位：Heinrich Heine University Dusseldorf; Leibniz Association; Deutsches Diabetes-Zentrum (DDZ)
  摘要：In recent work we introduced a general, a weak, and a strong partitioning principles for the construction of multiple decision procedures as multiple testing or selection procedures. Partitioning principles can be viewed as natural extensions of the closure principle and sometimes yield more powerful decision procedures. In this article we consider the problem of establishing equivalence with the best with respect to k treatment means, where equivalence is defined in terms of a threshold value...
• Testing for covariate effects in the fully nonparametric analysis of covariance model AI翻译
  作者：Wang, Lan; Akritas, Michael G.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Traditional inference questions in the analysis of covariance mainly focus on comparing different factor levels by adjusting for the continuous covariates, which are believed to also exert a significant effect on the outcome variable. Testing hypotheses about the covariate effects, although of substantial interest in many applications, has received relatively limited study in the semiparametric/nonparametric setting. In the context of the fully nonparametric analysis of covariance model of Akr...
• Advanced distribution theory for SiZer AI翻译
  作者：Hannig, J.; Marron, J. S.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of North Carolina; University of North Carolina Chapel Hill
  摘要：SiZer is a powerful method for exploratory data analysis. In this article approximations to the distributions underlying the simultaneous statistical inference are investigated, and large improvements are made in the approximation using extreme value theory. This results in improved size, and also in an improved global inference version of SiZer. The main points are illustrated with real data and simulated examples.
• Outlier detection in multivariate time series by projection pursuit AI翻译
  作者：Galeano, Pedro; Pena, Daniel; Tsay, Ruey S.
  作者单位：Universidad Carlos III de Madrid; University of Chicago
  摘要：In this article we use projection pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions can be more powerful than testing the multivariate series directly. The optimal directions for detecting outliers are found by numerical optimization of the kurtosis coefficient of the projected series. We propose an iterative procedure to detect and handle multiple outliers based on a univariate search in...
• Bayesian-optimal design via interacting particle systems AI翻译
  作者：Amzal, Billy; Bois, Frederic Y.; Parent, Eric; Robert, Christian R.
  作者单位：Universite PSL; Universite Paris-Dauphine
  摘要：We propose a new stochastic algorithm for Bayesian-optimal design in nonlinear and high-dimensional contexts. Following Peter Muller, we solve an optimization problem by exploring the expected utility surface through Markov chain Monte Carlo simulations. The optimal design is the mode of this surface considered a probability distribution. Our algorithm relies on a particle method to efficiently explore high-dimensional multimodal surfaces, with simulated annealing to concentrate the samples ne...
• Inference in semiparametric dynamic models for binary longitudinal data AI翻译
  作者：Chib, Siddhartha; Jeliazkov, Ivan
  作者单位：Washington University (WUSTL); University of California System; University of California Irvine
  摘要：This article deals with the analysis of a hierarchical sermparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N-3), where N is the total number of observations in the sample. Therefore, nonparametric estimation is largely infeasible when the sample size is large, as in typical in the longitudinal setti...
• Random forests and adaptive nearest neighbors AI翻译
  作者：Lin, Yi; Jeon, Yongho
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：In this article we study random forests through their connection with a new framework of adaptive nearest-neighbor methods. We introduce a concept of potential nearest neighbors (k-PNNs) and show that random forests can be viewed as adaptively weighted k-PNN methods. Various aspects of random forests can be studied from this perspective. We study the effect of terminal node sizes on the prediction accuracy of random forests. We further show that random forests with adaptive splitting schemes a...
• Non-gaussian Bayesian geostatistical modeling AI翻译
  作者：Palacios, M. Blanca; Steel, Mark F. J.
  作者单位：University of Basque Country; University of Warwick
  摘要：Sampling models for geostatistical data are usually based on Gaussian processes. However, real data often display non-Gaussian features, such as heavy tails. In this article we propose a more flexible class of sampling models. We start from the spatial linear model that has a spatial trend plus a stationary Gaussian error process. We extend the sampling model to non-Gaussianity by including a scale parameter at each location. We make sure that we obtain a valid stochastic process. The scale pa...