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作者：Chen, Yu-Chun; Cheng, Ming-Yen; Wu, Hau-Tieng
作者单位：National Yang Ming Chiao Tung University; National Taiwan University; University of California System; University of California Berkeley
摘要：Periodicity and trend are features describing an observed sequence, and extracting these features is an important issue in many scientific fields. However, it is not an easy task for existing methods to analyse simultaneously the trend and dynamics of the periodicity such as time varying frequency and amplitude, and the adaptivity of the analysis to such dynamics and robustness to heteroscedastic dependent errors are not guaranteed. These tasks become even more challenging when there are multi...
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作者：Chiou, Jeng-Min; Mueller, Hans-Georg
作者单位：Academia Sinica - Taiwan; University of California System; University of California Davis
摘要：Multivariate functional data are increasingly encountered in data analysis, whereas statistical models for such data are not well developed yet. Motivated by a case-study where one aims to quantify the relationship between various longitudinally recorded behaviour intensities for Drosophila flies, we propose a functional linear manifold model. This model reflects the functional dependence between the components of multivariate random processes and is defined through data-determined linear comb...
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作者：Zheng, Zemin; Fan, Yingying; Lv, Jinchi
作者单位：University of Southern California
摘要：High dimensional sparse modelling via regularization provides a powerful tool for analysing large-scale data sets and obtaining meaningful interpretable models. The use of non-convex penalty functions shows advantage in selecting important features in high dimensions, but the global optimality of such methods still demands more understanding. We consider sparse regression with a hard thresholding penalty, which we show to give rise to thresholded regression. This approach is motivated by its c...
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作者：Frick, Klaus; Munk, Axel; Sieling, Hannes
作者单位：University of Gottingen; Max Planck Society
摘要：We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE, for the change point problem in exponential family regression. An unknown step function is estimated by minimizing the number of change points over the acceptance region of a multiscale test at a level alpha. The probability of overestimating the true number of change points K is controlled by the asymptotic null distribution of the multiscale test statistic. Further, we derive exponential bounds for the pr...
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作者：Spiegelhalter, David J.; Best, Nicola G.; Carlin, Bradley P.; van der Linde, Angelika
作者单位：University of Cambridge; Imperial College London; University of Minnesota System; University of Minnesota Twin Cities
摘要：The essentials of our paper of 2002 are briefly summarized and compared with other criteria for model comparison. After some comments on the paper's reception and influence, we consider criticisms and proposals forimprovement made by us and others.
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作者：Zhu, Hongxiao; Yao, Fang; Zhang, Hao Helen
作者单位：Virginia Polytechnic Institute & State University; University of Toronto; University of Arizona
摘要：Functional additive models provide a flexible yet simple framework for regressions involving functional predictors. The utilization of a data-driven basis in an additive rather than linear structure naturally extends the classical functional linear model. However, the critical issue of selecting non-linear additive components has been less studied. In this work, we propose a new regularization framework for structure estimation in the context of reproducing kernel Hilbert spaces. The approach ...
