3期

最新文章

• Hysteretic autoregressive time series models AI翻译
  作者：Li, Guodong; Guan, Bo; Li, Wai Keung; Yu, Philip L. H.
  作者单位：University of Hong Kong
  摘要：This paper extends the classical two-regime threshold autoregressive model by introducing hysteresis to its regime-switching structure, which leads to a new model: the hysteretic autoregressive model. The proposed model enjoys the piecewise linear structure of a threshold model but has a more flexible regime switching mechanism. A sufficient condition is given for geometric ergodicity. Conditional least squares estimation is discussed, and the asymptotic distributions of its estimators and inf...
• Outlier detection for high-dimensional data AI翻译
  作者：Ro, Kwangil; Zou, Changliang; Wang, Zhaojun; Yin, Guosheng
  作者单位：Nankai University; University of Hong Kong
  摘要：Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver r...
• Covariance-based analyses of biological pathways AI翻译
  作者：Danaher, P.; Paul, D.; Wang, P.
  作者单位：NanoString Technologies; University of California System; University of California Davis; Icahn School of Medicine at Mount Sinai
  摘要：The use of high-throughput data to study the changing behaviour of biological pathways has focused mainly on examining the changes in the means of pathway genes. In this paper, we propose instead to test for changes in the co-regulated and unregulated variability of pathway genes. We assume that the eigenvalues of previously defined pathways capture biologically relevant quantities, and we develop a test for biologically meaningful changes in the eigenvalues between classes. This test reflects...
• Entropy testing for nonlinear serial dependence in time series AI翻译
  作者：Giannerini, Simone; Maasoumi, Esfandiar; Dagum, Estela Bee
  作者单位：University of Bologna; Emory University
  摘要：We propose tests for nonlinear serial dependence in time series under the null hypothesis of general linear dependence, in contrast to the more widely studied null hypothesis of independence. The approach is based on combining an entropy dependence metric, which possesses many desirable properties and is used as a test statistic, with a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity, and a smoothed sieve bootstrap scheme. We show h...
• Bayesian sensitivity analysis with the Fisher-Rao metric AI翻译
  作者：Kurtek, Sebastian; Bharath, Karthik
  作者单位：University System of Ohio; Ohio State University; University of Nottingham
  摘要：We propose a geometric framework to assess sensitivity of Bayesian procedures to modelling assumptions based on the nonparametric Fisher-Rao metric. While the framework is general, the focus of this article is on assessing local and global robustness in Bayesian procedures with respect to perturbations of the likelihood and prior, and on the identification of influential observations. The approach is based on a square-root representation of densities, which enables analytical computation of ge...
• Efficient estimation of the number of false positives in high-throughput screening AI翻译
  作者：Rootzen, Holger; Zholud, Dmitrii
  作者单位：Chalmers University of Technology; University of Gothenburg
  摘要：This paper develops tail estimation methods to handle false positives in multiple testing problems where testing is done at extreme significance levels and with low degrees of freedom, and where the true null distribution may differ from the theoretical one. We show that the number of false positives, conditional on the total number of positives, has an approximately binomial distribution, and we find estimators of the distribution parameter. We also develop methods for estimation of the true ...
• Nonparametric Bayesian testing for monotonicity AI翻译
  作者：Scott, J. G.; Shively, T. S.; Walker, S. G.
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：This paper adopts a nonparametric Bayesian approach to testing whether a function is monotone. Two new families of tests are constructed. The first uses constrained smoothing splines with a hierarchical stochastic-process prior that explicitly controls the prior probability of monotonicity. The second uses regression splines together with two proposals for the prior over the regression coefficients. Via simulation, the finite-sample performance of the tests is shown to improve upon existing fr...
• On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions AI翻译
  作者：Wadsworth, Jennifer L.
  作者单位：Lancaster University
  摘要：Full likelihood-based inference for high-dimensional multivariate extreme value distributions, or max-stable processes, is feasible when incorporating occurrence times of the maxima; without this information, d-dimensional likelihood inference is usually precluded due to the large number of terms in the likelihood. However, some studies have noted bias when performing high-dimensional inference that incorporates such event information, particularly when dependence is weak. We elucidate this ph...
• Diagnostic studies in sufficient dimension reduction AI翻译
  作者：Chen, Xin; Cook, R. Dennis; Zou, Changliang
  作者单位：National University of Singapore; University of Minnesota System; University of Minnesota Twin Cities; Nankai University
  摘要：Sufficient dimension reduction in regression aims to reduce the predictor dimension by replacing the original predictors with some set of linear combinations of them without loss of information. Numerous dimension reduction methods have been developed based on this paradigm. However, little effort has been devoted to diagnostic studies within the context of dimension reduction. In this paper we introduce methods to check goodness-of-fit for a given dimension reduction subspace. The key idea is...
• Benchmarked empirical Bayes methods in multiplicative area-level models with risk evaluation AI翻译
  作者：Ghosh, M.; Kubokawa, T.; Kawakubo, Y.
  作者单位：State University System of Florida; University of Florida; University of Tokyo; University of Tokyo
  摘要：The paper develops hierarchical empirical Bayes and benchmarked hierarchical empirical Bayes estimators of positive small area means under multiplicative models. The usual benchmarking requirement is that the small area estimates, when aggregated, should equal the direct estimates for the larger geographical areas. However, while estimating positive small area parameters, the conventional squared error or weighted squared error loss subject to the usual benchmark constraint may not produce pos...