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This book systematically addresses the design and analysis of efficient techniques for independent random sampling.
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The focus on doing data mining rather than just reading about data mining is refreshing.The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment.
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This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain.
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This easy-to-follow textbook/reference presents a concise introduction to mathematical analysis from an algorithmic point of view, with a particular focus on applications of analysis and aspects of mathematical modelling.
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This proceedings volume contains eight selected papers thatwere presented... Læs mere
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This Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.
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The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometryfeatures.
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This book is an integrated treatment of applied statistical methods, presented at an intermediate level. It serves as an advanced introduction to the SAS programming language as well as demonstrating how to use SAS to analyse of a wide variety of data.
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In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical... Læs mere
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The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation.