| 527 | 38 |
|---|
| 4 Real Options Assessment Using Excel Based Tools | 528 |
| 5 Conclusions and Outlook | 531 |
| References | 532 |
| Exploring the Interaction Structure of Weblogs | 533 |
| 1 Introduction | 533 |
| 2 Identifying Blogs on the WWW | 534 |
| 2.1 Social Networks of Blogs | 534 |
| 2.2 Assessment of Egos and Ego Networks | 535 |
| 3 Empirical Application | 537 |
| 4 Conclusions and Future Work | 539 |
| References | 540 |
| Analyzing Preference Rankings when There AreToo Many Alternatives | 541 |
| 1 Introduction and Motivation | 541 |
| 2 Preliminaries | 542 |
| 3 Methodology | 543 |
| 3.1 Test Statistic | 545 |
| 3.2 Multiple Comparisons | 545 |
| 3.3 Rank Plots | 546 |
| 3.4 Homogeneous Subsets | 547 |
| 4 Illustration | 547 |
| 4.1 Data | 547 |
| 4.2 Results | 548 |
| 5 Conclusion | 550 |
| References | 550 |
| Considerations on the Impact of Ill-ConditionedConfigurations in the CML Approach | 551 |
| 1 Introduction | 551 |
| 2 The Partial Credit Model | 553 |
| 3 CML Approach to Estimate Item Parameters | 554 |
| 4 State of the Art Regarding Existence of ML Estimates | 555 |
| 5 Analysis of Fixed Small-Dimensional Datasets | 556 |
| 6 Concluding Remarks | 559 |
| References | 559 |
| Dyadic Interactions in Service Encounter:Bayesian SEM Approach | 561 |
| 1 Introduction | 561 |
| 1.1 Service Encounter in Relationship Marketing | 561 |
| 1.2 Research Design | 562 |
| 2 APIM Model: Bayesian SEM Approach | 563 |
| 2.1 Assumptions of Bayesian SEM | 563 |
| 2.2 APIM Structural Model | 564 |
| 3 Final Remarks | 569 |
| References | 569 |
| Part IX Archaeology and Spatial Planning | 571 |
|---|
| Estimating the Number of Buildings in Germany | 572 |
| 1 Introduction | 572 |
| 2 Inspection and Transformation of Data | 573 |
| 3 Estimation | 575 |
| 4 Information Optimisation | 578 |
| 5 Conclusion | 579 |
| References | 580 |
| Mapping Findspots of Roman Military Brickstampsin Mogontiacum (Mainz) and Archaeometrical Analysis | 581 |
| 1 Introduction | 581 |
| 2 Mapping of the Locations of Findspots | 583 |
| 3 Smooth Mapping by Nonparametric Density Estimation | 584 |
| 4 Comparison of Different Periods | 585 |
| 5 Conclusions | 589 |
| References | 589 |
| Analysis of Guarantor and Warrantee RelationshipsAmong Government Officials in theEighth Century in the Old Capital of Japanby Using Asymmetric Multidimensional Scaling | 590 |
| 1 Introduction | 590 |
| 2 Data | 591 |
| 3 The Method | 592 |
| 4 The Analysis and the Result | 593 |
| 5 Discussion | 595 |
| References | 599 |
| Analysis of Massive Emigration from Poland:The Model-Based Clustering Approach | 600 |
| 1 Introduction | 600 |
| 2 Model-Based Clustering | 601 |
| 2.1 Mixture Models | 601 |
| 2.2 Parameter Estimation and Model Selection | 602 |
| 2.3 Model-Based Strategy for Clustering | 603 |
| 3 Example | 604 |
| 4 Conclusions | 606 |
| 5 Discussion | 608 |
| References | 608 |
| Part X Bio- and Health Sciences | 610 |
|---|
| Systematics of Short-Range Correlations inEukaryotic Genomes | 611 |
| 1 Introduction | 611 |
| 2 Systematics of Correlation Signatures | 613 |
| 3 Algorithmic Challenges | 617 |
| 3.1 Systematic Comparison of Many Trees: The Tree-Color Coding Method | 617 |
| 3.2 Memory and Run Time Management for Large Genomes | 618 |
| 4 Conclusion | 620 |
| References | 621 |
| On Classification of Molecules and Species ofRepresentation Rings | 622 |
| 1 Introduction | 622 |
| 2 Classification of Molecules by Symmetry Groups | 623 |
| 3 Ordinary Representations of Finite Groups | 624 |
| 4 Modular Representations of Finite Groups | 626 |
| 5 Species of Representation Rings | 627 |
| 6 Conclusions | 631 |
| References | 631 |
| The Precise and Efficient Identification ofMedical Order Forms Using Shape Trees | 633 |
| 1 Introduction | 633 |
| 2 Geometrical Shapes for Determining Similarity | 634 |
| 2.1 Object Recognition | 634 |
| 2.2 Shapes as Models for Regions | 634 |
| 2.3 Modeling Regions as a Shape Tree | 635 |
| 2.4 Shape Tree Structure | 635 |
<
