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Titlebook: Exploratory Data Analysis in Empirical Research; Proceedings of the 2 Manfred Schwaiger,Otto Opitz Conference proceedings 2003 Springer-Ver

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41#
發(fā)表于 2025-3-28 15:38:43 | 只看該作者
Tests for One-dimensional Nearest Neighbors Clustershis way clusters of points are generated which are connected by arrows. (.) studied the distribution of the number (.) of NN clusters. Because too small or too large values of M may indicate deviations from the null hypothesis of no clustering, tests for the detection of clusters can be based on the
42#
發(fā)表于 2025-3-28 20:35:45 | 只看該作者
43#
發(fā)表于 2025-3-29 02:30:25 | 只看該作者
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發(fā)表于 2025-3-29 06:37:58 | 只看該作者
45#
發(fā)表于 2025-3-29 07:16:38 | 只看該作者
46#
發(fā)表于 2025-3-29 13:37:48 | 只看該作者
Bayesian Latent Class Metric Conjoint Analysis — A Case Study from the Austrian Mineral Water Markets. The approach starts with estimating unidentified models for various numbers of classes. Exact Bayes’ factors are computed by the bridge sampling estimator to compare different models and select the number of classes. Estimation of the unidentified model is carried out using the random permutation
47#
發(fā)表于 2025-3-29 17:43:49 | 只看該作者
https://doi.org/10.1007/978-0-387-33535-3 to assign explicit dates to a seriated solution. This new method of seriation is applied to a series of ceramic assemblages consisting of the locally produced tableware from Sagalassos (SW Turkey). These tableware assemblages have already been seriated and dated independently via empirical archaeol
48#
發(fā)表于 2025-3-29 22:35:28 | 只看該作者
Constrained Correspondence Analysis for Seriation in Archaeology Applied to Sagalassos Ceramic Table to assign explicit dates to a seriated solution. This new method of seriation is applied to a series of ceramic assemblages consisting of the locally produced tableware from Sagalassos (SW Turkey). These tableware assemblages have already been seriated and dated independently via empirical archaeol
49#
發(fā)表于 2025-3-30 00:12:22 | 只看該作者
50#
發(fā)表于 2025-3-30 06:23:53 | 只看該作者
Jason Valent,Charles A. Schifferiers and partitions the remaining . observations in . clusters. We also propose an algorithm that approximates this estimator. We study under which conditions this estimator is a maximum likelihood estimator.
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