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作者：Biswas, Munmun; Mukhopadhyay, Minerva; Ghosh, Anil K.
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Indian Statistical Institute; Indian Statistical Institute Kolkata
摘要：We propose a multivariate generalization of the univariate two-sample run test based on the shortest Hamiltonian path. The proposed test is distribution-free in finite samples. While most existing two-sample tests perform poorly or are even inapplicable to high-dimensional data, our test can be conveniently used in high-dimension, low-sample-size situations. We investigate its power when the sample size remains fixed and the dimension of the data grows to infinity. Simulated and real datasets ...
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作者：Maruyama, Yuzo; Strawderman, William E.
作者单位：University of Tokyo; Rutgers University System; Rutgers University New Brunswick
摘要：This paper studies Bayesian variable selection in linear models with general spherically symmetric error distributions. We construct the posterior odds based on a separable prior, which arises as a class of mixtures of Gaussian densities. The posterior odds for comparing among nonnull models are shown to be independent of the error distribution, if this is spherically symmetric. Because of this invariance, we refer to our method as a robust Bayesian variable selection method. We demonstrate th...
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作者：Delaigle, A.; Hall, P.; Wishart, J. R.
作者单位：University of Melbourne; University of New South Wales Sydney
摘要：We consider nonparametric and semiparametric estimation of a conditional probability curve in the case of group testing data, where the individuals are pooled randomly into groups and only the pooled data are available. We derive a nonparametric weighted estimator that has optimality properties accounting for group sizes, and show how to extend it to multivariate settings, including the partially linear model. In the group testing context, it is natural to assume that the probability curve dep...
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作者：Vexler, A.; Tao, G.; Hutson, A. D.
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY
摘要：Posterior expectation is widely used as a Bayesian point estimator. In this note we extend it from parametric models to nonparametric models using empirical likelihood, and develop a nonparametric analogue of James Stein estimation. We use the Laplace method to establish asymptotic approximations to our proposed posterior expectations, and show by simulation that they are often more efficient than the corresponding classical nonparametric procedures, especially when the underlying data are ske...
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作者：Vogt, Michael; Linton, Oliver
作者单位：University of Cambridge
摘要：We investigate a nonparametric regression model including a periodic component, a smooth trend function, and a stochastic error term. We propose a procedure to estimate the unknown period and the function values of the periodic component as well as the nonparametric trend function. The theoretical part of the paper establishes the asymptotic properties of our estimators. In particular, we show that our estimator of the period is consistent. In addition, we derive the convergence rates and the ...
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作者：Barnett, Ian J.; Lin, Xihong
作者单位：Harvard University
摘要：The higher criticism test is effective for testing a joint null hypothesis against a sparse alternative, e.g., for testing the effect of a gene or genetic pathway that consists of d genetic markers. Accurate p-value calculations for the higher criticism test based on the asymptotic distribution require a very large d, which is not the case for the number of genetic variants in a gene or a pathway. In this paper we propose an analytical method for accurately computing the p-value of the higher ...
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作者：Georgiou, S. D.; Stylianou, S.; Drosou, K.; Koukouvinos, C.
作者单位：Royal Melbourne Institute of Technology (RMIT); University of Aegean; National Technical University of Athens
摘要：This paper presents new infinite families of orthogonal designs for computer experiments. In cases where orthogonal 'designs cannot exist, we construct alternative, nearly orthogonal designs. Our designs can accommodate many factors and a large set of levels. No iterative computer search is required. To build up the desired orthogonal designs we develop and use new infinite classes of periodic Golay pairs.
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作者：Wang, D.; McMahan, C. S.; Gallagher, C. M.; Kulasekera, K. B.
作者单位：Clemson University; University of Louisville
摘要：Group testing, through the use of pooling, has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest, such as infection status. A topic of key interest in the statistical literature involves the development of regression models that relate individual-level covariates to testing responses observed from pooled specimens. In this article, we propose a general semiparametric framework that allows for the inclusion of multi-...
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作者：Sabbaghi, Arman; Dasgupta, Tirthankar; Wu, C. F. Jeff
作者单位：Harvard University; University System of Georgia; Georgia Institute of Technology
摘要：Indicator functions are constructed under the linear-quadratic parameterization for contrasts, and applied to the study of partial aliasing properties for three-level fractional factorial designs. An algebraic operation is introduced for the calculation of indicator function coefficients. This operation connects design construction methods to the analysis under the linear-quadratic system, and helps establish simple conditions for the estimability of interactions.
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作者：Kaufman, S.; Rosset, S.
作者单位：Tel Aviv University
摘要：Regularization aims to improve prediction performance by trading an increase in training error for better agreement between training and prediction errors, which is often captured through decreased degrees of freedom. In this paper we give examples which show that regularization can increase the degrees of freedom in common models, including the lasso and ridge regression. In such situations, both training error and degrees of freedom increase, making the regularization inherently without meri...
