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作者：Kong, Xin-Bing; Liu, Zhi; Jing, Bing-Yi
作者单位：Soochow University - China; Soochow University - China; University of Macau; Hong Kong University of Science & Technology
摘要：Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the underlying process of h...
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作者：Liu, Haoyang; Aue, Alexander; Paul, Debashis
作者单位：University of California System; University of California Berkeley; University of California System; University of California Davis
摘要：This paper is concerned with extensions of the classical Marcenko-Pastur law to time series. Specifically, p-dimensional linear processes are considered which are built from innovation vectors with independent, identically distributed (real- or complex-valued) entries possessing zero mean, unit variance and finite fourth moments. The coefficient matrices of the linear process are assumed to be simultaneously diagonalizable. In this setting, the limiting behavior of the empirical spectral distr...
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作者：Yang, Yanrong; Pan, Guangming
作者单位：Monash University; Nanyang Technological University
摘要：This paper proposes a new statistic to test independence between two high dimensional random vectors X: p(1) x 1 and Y : p(2) x 1. The proposed statistic is based on the sum of regularized sample canonical correlation coefficients of X and Y. The asymptotic distribution of the statistic under the null hypothesis is established as a corollary of general central limit theorems (CLT) for the linear statistics of classical and regularized sample canonical correlation coefficients when p(1) and p(2...
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作者：Schiebinger, Geoffrey; Wainwright, Martin J.; Yu, Bin
作者单位：University of California System; University of California Berkeley
摘要：Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clustering in recovering the latent labels of i.i.d. samples from a finite mixture of nonparametric distributions. The difficulty of this label recovery problem depends on the overlap...
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作者：Lee, Young K.; Mammen, Enno; Nielsen, Jens P.; Park, Byeong U.
作者单位：Kangwon National University; Ruprecht Karls University Heidelberg; City St Georges, University of London; Seoul National University (SNU)
摘要：This paper generalizes recent proposals of density forecasting models and it develops theory for this class of models. In density forecasting, the density of observations is estimated in regions where the density is not observed. Identification of the density in such regions is guaranteed by structural assumptions on the density that allows exact extrapolation. In this paper, the structural assumption is made that the density is a product of one-dimensional functions. The theory is quite gener...
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作者：Jacob, Pierre E.; Thiery, Alexandre H.
作者单位：University of Oxford; National University of Singapore
摘要：We study the existence of algorithms generating almost surely nonnegative unbiased estimators. We show that given a nonconstant real-valued function f and a sequence of unbiased estimators of lambda is an element of R, there is no algorithm yielding almost surely nonnegative unbiased estimators of f(lambda) is an element of R+. The study is motivated by pseudo-marginal Monte Carlo algorithms that rely on such nonnegative unbiased estimators. These methods allow exact inference in intractable m...
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作者：Fan, Jianqing; Xue, Lingzhou; Zou, Hui
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
