Advances in Data Analysis: Proceedings of the 30th Annual - download pdf or read online

By Gilles Celeux (auth.), Professor Dr. Reinhold Decker, Professor Dr. Hans -J. Lenz (eds.)

This e-book makes a speciality of exploratory facts research, studying of latent constructions in datasets, and unscrambling of data. It covers a huge diversity of tools from multivariate facts, clustering and category, visualization and scaling in addition to from facts and time sequence research. It offers new ways for info retrieval and knowledge mining. moreover, the e-book reviews hard functions in advertising and administration technology, banking and finance, bio- and overall healthiness sciences, linguistics and textual content research, statistical musicology and sound class, in addition to archaeology. particular emphasis is wear interdisciplinary learn and the interplay among thought and practice.

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Read Online or Download Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8–10, 2006 PDF

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Additional info for Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft fur Klassifikation e.V., Freie Universitat Berlin, March 8–10, 2006

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J. (1975): Measuring the Power of Hierarchical Cluster Analysis. Journal of the American Statistical Association, 70, 349, 3138. B. J. (1965): ISODATA, A Novel Method of Data Analysis and Pattern Classification. Tech. Rep. AD 699616, Stanford Research Institute, Menlo Park. -H. and DIDAY, E. (2000): Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data. Springer, Berlin. B. and HARABASZ, J. (1974): A Dendrite Method for Cluster Analysis. Communications in Statistics, 3, 1-27.

S. (1990): Mixed Markov and Latent Markov Modelling Applied to Brand Choice Behavior. International Journal of Research in Marketing, 7, 5– 19. J. T. (1993): An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data. Marketing Science, 12, 103–124. SCHWARZ, G. (1978): Estimating the Dimension of a Model. Annals of Statistics, 6, 461–464. S. (1938): The Large Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses. Annals of Mathematical Statistics, 9, 60–62.

28 Jos´e G. Dias Table 2. 000 A second objective of the study was the comparison of these criteria across the design factors. Increasing the sample size always improves the performance of the information criteria, and reduces underfitting. However, for AIC increas- Model Selection for Mixtures of Markov Chains 29 ing the sample size tends to increase the overfitting. Increasing the number of time points (T + 1) improves the performance of the information criteria and reduces the underfitting. Increasing the state space (K) reduces the underfitting, and improves the performance of the information criteria.

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