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Titlebook: Statistical Theory and Computational Aspects of Smoothing; Proceedings of the C Wolfgang Härdle,Michael G. Schimek Conference proceedings 1

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书目名称Statistical Theory and Computational Aspects of Smoothing
副标题Proceedings of the C
编辑Wolfgang Härdle,Michael G. Schimek
视频videohttp://file.papertrans.cn/877/876656/876656.mp4
丛书名称Contributions to Statistics
图书封面Titlebook: Statistical Theory and Computational Aspects of Smoothing; Proceedings of the C Wolfgang Härdle,Michael G. Schimek Conference proceedings 1
描述One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
出版日期Conference proceedings 1996
关键词Glättung; Kernel; Statistische Modelierung; Variance; calculus; econometrics; economics; invariance; modelin
版次1
doihttps://doi.org/10.1007/978-3-642-48425-4
isbn_softcover978-3-7908-0930-5
isbn_ebook978-3-642-48425-4Series ISSN 1431-1968 Series E-ISSN 2628-8966
issn_series 1431-1968
copyrightPhysica-Verlag Heidelberg 1996
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Rejoinder, must address the methodology as it is used in practice, and set criteria that are of genuine importance for that usage. Having set the premises, the investigator must then derive results. This requires command of the necessary technical tools.
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Nonparametric Estimation of Additive Separable Regression Models, normality of the estimator is proved. We also investigate a variable selection procedure using the proposed estimator and prove that asymptotically the procedure finds the correct variable set with probability 1. A simulation study is presented investigating the practical performance of the procedure.
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Conference proceedings 1996pics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
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Mean and Dispersion Additive Models,cribed and is implemented in GLIM4 allowing flexible and interactive modelling of both the mean and dispersion of a dependent variable. Two examples are given to demonstrate the use of the MADAM for modelling overdispersion in each of Poisson regression model and a Binomial logistic regression model.
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