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Titlebook: COMPSTAT; Proceedings in Compu David Edwards,Niels E. Raun Conference proceedings 1988 Physica-Verlag Heidelberg 1988 ANOVA.Generalized lin

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書目名稱COMPSTAT
副標(biāo)題Proceedings in Compu
編輯David Edwards,Niels E. Raun
視頻videohttp://file.papertrans.cn/233/232009/232009.mp4
圖書封面Titlebook: COMPSTAT; Proceedings in Compu David Edwards,Niels E. Raun Conference proceedings 1988 Physica-Verlag Heidelberg 1988 ANOVA.Generalized lin
描述The papers assembled in this volume were presented at COMPSTAT 1988, the 8th biannual Symposium in Computational Statistics held under the auspices of the International Association for Statistical Computing. The current impact of computers on the theory and practice of statistics can be traced at many levels: on one level, the ubiquitous personal computer has made methods for explorative data analysis and display, rarely even described in conventional statistics textbooks, widely available. At another level, advances in computing power permit the development and application of statistical methods in ways that previously have been infeasible. Some of these methods, for example Bayesian methods, are deeply rooted in the philosophical basis of statistics, while others, for example dynamic graphics, present the classical statistical framework with quite novel perspectives. The contents of this volume provide a cross-section of current concerns and interests in computational statistics. A dominating topic is the application of artificial intelligence to statistics (and vice versa), where systems deserving the label" expert systems" are just beginning to emerge from the haze of good inte
出版日期Conference proceedings 1988
關(guān)鍵詞ANOVA; Generalized linear model; Projection Pursuit; Regression analysis; Resampling; Time series; Varianc
版次1
doihttps://doi.org/10.1007/978-3-642-46900-8
isbn_softcover978-3-7908-0411-9
isbn_ebook978-3-642-46900-8
copyrightPhysica-Verlag Heidelberg 1988
The information of publication is updating

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Efficient Nonparametric Smoothing in High Dimensions Using Interactive Graphical Techniquespplied workers in biostatistics, economics and engineering to model the data in a nonparametric fashion. The benefits of this more flexible modeling come at the cost of greater computation, especially in high dimensions. In this paper several possibilities of smoothing in high dimensions are describ
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Detecting Structures by Means of Projection Pursuitnlmodal densities mixture. It is shown that under this model the use of projections indices based on Renyi entropy or on third or fourth moments results In obtaining an estimate of the discriminant subspace. For estimating the Renyi indices values some forms of the order statistics are used. For det
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Graphical Modelling with Large Numbers of Variables: An Application of Principal Componentsting is proposed in the context of covariance selection models, similar in spirit to the screening procedure of Kreiner (1987), which avoids the explicit fitting of any graphical model. It is conjectured that a useful guide to assess the performance of the model is to compare its predictive power ag
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