Titlebook: Computer Intensive Methods in Statistics; Wolfgang H?rdle,Léopold Simar Conference proceedings 1993 Springer-Verlag Berlin Heidelberg 1993

MITE

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MIR

泛濫

文藝

Intensive Numerical and Symbolic Computing in Parametric Test Theoryl regions with particular optimality properties, . computing to obtain a better understanding of a statistical model e.g. by giving the geometry of the model. Each level is explained in the construction of two tests for a simple null hypothesis in two lifetime models..The .-LMMPUM. test maximizes th

序曲

Learning Data Analysis and Mathematical Statistics with a Macintoshtics and the practice of data analysis. The authors developed a set of lectures notes with a strong emphasis on: Graphical analyses, random number generation, simulation techniques, resampling methods, dynamic illustration of regression diagnostics, robust methods ... . Most of these concepts can be

大包裹

Markov Random Field Models in Image Remote Sensingontext of conditional maximum likelihood estimation. Here, in the same context, we propose some original uses of Mrf, especially in image segmentation, noise filtering and discriminant analysis. For instance, we propose a Mrf model on the spectral signatures space, a strongly unified approach to cla

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Bandwidth Selection for Kernel Regression: a Surveye. the bandwidth). Bandwidth choice turns out to be of particular importance as well for practical use as to insure good asymptotic properties of the estimate. Various techniques have been proposed in the past ten last years to select optimal values of this parameter. This paper presents a survey on

beta-carotene

Application of Resampling Methods to the Choice of Dimension in Principal Component Analysissessing the stability of the fit is considered. The choice of dimension then amounts to the minimisation of an expected loss which has to be estimated. This is achieved by resampling methods. Different bootstrap and jackknife estimates are presented. The behaviour of these estimates are investigated