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Titlebook: Nonparametric Regression Analysis of Longitudinal Data; Hans-Georg Müller Book 1988 Springer-Verlag Berlin Heidelberg 1988 Estimator.Regre

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書目名稱Nonparametric Regression Analysis of Longitudinal Data
編輯Hans-Georg Müller
視頻videohttp://file.papertrans.cn/668/667829/667829.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Nonparametric Regression Analysis of Longitudinal Data;  Hans-Georg Müller Book 1988 Springer-Verlag Berlin Heidelberg 1988 Estimator.Regre
描述This monograph reviews some of the work that has been done for longitudi- nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author‘s Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpu
出版日期Book 1988
關(guān)鍵詞Estimator; Regression analysis; Variance; average; optimization; statistics
版次1
doihttps://doi.org/10.1007/978-1-4612-3926-0
isbn_softcover978-0-387-96844-5
isbn_ebook978-1-4612-3926-0Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 1988
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Kernel and Local Weighted Least Squares Methods,It is assumed from now on that in the model (2.1) . where usually indices n are omitted, 0≤t.≤t.≤…≤t.≤l without loss of generality. As in (2.1), the errors are assumed to be i.i.d. with Eε.=0, Eε.= .∞ (for most considerations in Chapters 4–6, uncorrelatedness is sufficient).
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Optimization of Kernel and Weighted Local Regression Methods,Optimization here means minimization of the asymptotically leading term of the IMSE. Since the asymptotic expression for the IMSE is the same for both kernel and weighted local least squares methods, optimization considerations are also the same.
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Consistency Properties of Moving Weighted Averages,We consider here the usual fixed design regression model . with triangular array errors ε. . i.i.d. for each n, Eε. = 0 and g: A → ?, A ? ?., corresponding to the multivariate case (6.1). The notation and assumptions are the same as in 6.1.
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Nonparametric Regression Analysis of Longitudinal Data
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late some of the presently available antiarrhythmic agents to their possible modes of action on impulse formation and conduction. Previously documented data will be summarized and newer information presented to define the present state of our knowledge with the hope that this will serve as a basis for future investigation.
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