Titlebook: COMPSTAT; Proceedings in Compu Roger Payne,Peter Green Conference proceedings 1998 Springer-Verlag Berlin Heidelberg 1998 Approximation.Int

Addiction

书目名称COMPSTAT影响因子(影响力)




书目名称COMPSTAT影响因子(影响力)学科排名




书目名称COMPSTAT网络公开度




书目名称COMPSTAT网络公开度学科排名




书目名称COMPSTAT被引频次




书目名称COMPSTAT被引频次学科排名




书目名称COMPSTAT年度引用




书目名称COMPSTAT年度引用学科排名




书目名称COMPSTAT读者反馈




书目名称COMPSTAT读者反馈学科排名

积极词汇

Wavelets in Statistics: Some Recent Developmentsts of wavelets are reviewed, concentrating on the discrete wavelet transform, because of its relevance to practical and computational statisticians. Several recent areas of research are discussed, concentrating on extensions of the standard paradigm. A Bayesian approach is natural, because of the no

STALL

NATAL

Design Algorithms for Correlated Datariments which incorporate a correlation structure for errors or over the plots are becoming more generally applied. In this paper the design of experiments of a two-dimensional layout (rows and columns) with a spatial process is investigated. Algorithms based on simulated annealing and Tabu search a

deficiency

用肘

Optimal Scaling Methods for Graphical Display of Multivariate Data (or categorical) multivariate data will be considered, where observed variables are only partially known, and dealt with by using an optimal scaling framework that replaces nominal and ordinal variables by optimally quantified variables. The approach to graphical display that is advocated is closel

用肘

MCMC Specifics for Latent Variable Modelsd statistical methods. These connections go both ways: latent variable models were instrumental in designing these new methods, whose convergence properties and convergence diagnostic tools are specific to these models, and hybrid methods like simulated maximum likelihood primarily apply in such set

Chandelier

易受刺激

Exploring Time Series Using Semi- and Nonparametric Methodslification, a linearity test ought to be implemented at an early stage. If linearity . rejected, one possibility is to search for a parametric model among such classical models as the threshold, the exponential autoregressive, the bilinear, or the more flexible class of smooth transition models (cf.

prediabetes

Time Series Forecasting by Principal Component Methodsuses weighted multiple linear regression among principal components whereas the second one applies Kalman filtering on approximate state-space models. The forecasting performance of both methods is discussed on a real financial time-series.