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作者：Chan, Hock Peng; Loh, Wei-Liem
作者单位：National University of Singapore
摘要：This article contains two main theoretical results on neural spike train models, using the counting or point process on the real line as a model for the spike train. The first part of this article considers template matching of multiple spike trains. P-values for the occurrences of a given template or pattern in a set of spike trains are computed using a general scoring system. By identifying the pattern with an experimental stimulus, multiple spike trains can be deciphered to provide useful i...
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作者：Friedlander, Michael P.; Saunders, Michael A.
作者单位：University of British Columbia; Stanford University
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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Lee, Sikyum; Zhang, Heping
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Chinese University of Hong Kong; Yale University
摘要：Cook's [J. Roy. Statist. Soc. Ser. B 48 (1986) 133-169] local influence approach based on normal curvature is an important diagnostic tool for assessing local influence of minor perturbations to a statistical model. However, no rigorous approach has been developed to address two fundamental issues: the selection of an appropriate perturbation and the development of influence measures for objective functions at a point with a nonzero first derivative. The aim of this paper is to develop a diffe...
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作者：Candes, Emmanuel; Tao, Terence
作者单位：California Institute of Technology; University of California System; University of California Los Angeles
摘要：In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = X beta + z, where beta epsilon R-p is a parameter vector of interest, X is a data matrix with possibly far fewer rows than columns, n << p, and the z(i)'s are i.i.d. N(0, sigma(2)). Is it possible to estimate beta reliably based on the noisy data y? To estimate beta, we introduce a new estimator-we call it the Dantzig s...
