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By Ho P.-G.P. (ed.)

ISBN-10: 9533072288

ISBN-13: 9789533072289

ISBN-10: 9535108174

ISBN-13: 9789535108177

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Note that (13) is a generalization of the dissimilarity index introduced in Chouakria & Na‐ gabhushan, (2007). The dissimilarity index (13) can capture high-order serial correlations be‐ tween the sequences because the distance lag h is arbitrary, while Chouakria's index only captures the first-order correlation. The dependence of (13) on h is crucial, and in some specific cases, h can be chosen using an optimal criterion. In other words, for those processes in which the asymptotic variance of the codispersion coefficient is known, we suggest setting the value of ^ h to produce the minimum variance.

5772/50513 In the next section, we present two simulation examples to illustrate the capabilities of the hierarchical methods using the distance measure (13) under the tuning function given by (14). All else being constant, the clusters produced using traditional distances are usually different from those yielded using the distance measure (13). 4. Simulations In this example, we simulate observations from six first-order autoregressive models to il‐ lustrate the clustering produced by hierarchical methods when the sequences exhibit serial correlation.

Codispersion coefficient for spatial and temporal series. Statistics and Probability Letters, 78(11), 1290 -1300 . , & Mardesic, T. (2004). A recursive algorithm to restore images based on robust estimation of NSHP autoregressive models. Journal of Computational and Graphical Statistics, 13(3), 674 -682 . , & Garcia-Donato, G. (2006). Bayesian analysis of contaminated quarter plane moving average models. Journal of Statistical Computation and Simulation, 76(2), 131-147. [33] Vallejos, R. (2008).

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Advances in image segmentation by Ho P.-G.P. (ed.)

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