This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often u...
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ISBN:
(数字)9789811000775
ISBN:
(纸本)9789811000768
This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. 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. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.
Das Buch richtet sich an diejenigen, die Statistik in wirtschaftswissenschaftlich orientierten Studiengängen studieren. Der leicht verständliche Text ist mit vielen Beispielen und Übungen ergänzt. ...
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ISBN:
(数字)9783642373527
Das Buch richtet sich an diejenigen, die Statistik in wirtschaftswissenschaftlich orientierten Studiengängen studieren. Der leicht verständliche Text ist mit vielen Beispielen und Übungen ergänzt. Die praxisnahe Darstellung der Methoden wird durch die Erklärung und Anwendung der Statistikprogramme R (open source Progamm) und SPSS vervollständigt. Im Text sind für beide Programme viele Programmanweisungen enthalten. Die Autoren haben kompakt alle elementaren statistischen Verfahren für die Ökonomie anschaulich erklärt.
This volume focuses on innovative approaches and recent developments in clustering, analysis of data and models, and applications: The first part of the book covers a broad range of innovations in the area of clusteri...
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ISBN:
(数字)9783319012643
ISBN:
(纸本)9783319012636
This volume focuses on innovative approaches and recent developments in clustering, analysis of data and models, and applications: The first part of the book covers a broad range of innovations in the area of clustering, from algorithmic innovations for graph clustering to new visualization and evaluation techniques. The second part addresses new developments in data and decision analysis (conjoint analysis, non-additive utility functions, analysis of asymmetric relationships, and regularization techniques). The third part is devoted to the application of innovative data analysis methods in the life-sciences, the social sciences and in engineering. All contributions in this volume are revised and extended versions of selected papers presented in the German/Japanese Workshops at Karlsruhe (2010) and Kyoto (2012).
There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number...
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ISBN:
(数字)9780387713854
There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can • write short scripts to de?ne a Bayesian model • use or write functions to summarize a posterior distribution • use functions to simulate from the posterior distribution • construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).
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