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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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J. (1996). Some results on unilateral ARMA laticce processes. Journal of Statistical Planning and Inference, 50(3), 395-411. [24] Matheron, G. (1965). Les Variables Régionalisées et leur Estimation Masson Paris. [25] Politis, D. , & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303 -1313. [26] Ojeda, S. , Vallejos, R. , & Lucini, M. (2002). Performance of RA Estimator for Bidimensional Autoregressive Models. Journal of Statistical Simulation and Computa‐ tion, 72(1), 47 -62 .

8] Chouakria, A. , & Nagabhushan, P. N. (2007). Adaptive dissimilarity for measur‐ ing time series proximity. Advances in Data Analysis and Classification, 1(1), 5 -21 . , & Hémon, D. (1989). Assessing the significance of the cor‐ relation between two spatial processes. Biometrics, 45(1), 123 -134. [10] Cullis, B. , & Glesson, A. C. (1991). Spatial analysis of field experiments-an exten‐ sion to two dimensions. Biometrics, 47(4), 1449-1460. , & Friedlander, B. (1998). Parameter estimation of two-dimensional mov‐ ing average random fields.

However, hierarchi‐ cal methods using (13) yield the expected results, combining sequences X 1, X 2, and X 3be‐ fore the rest of the series. To obtain better insight into the classification process using the proposed distance measure (13), we carried out a second simulation study that involves clustering measures based on other distances (but using the same setup). Observations from models 1-6 were generated using Gaussian white noise sequences for the errors, thereby preserving the same correla‐ tion structure used in the first study.

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

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