"Computational Statistics and Applications" ed. by Ricardo López-Ruiz

This volume examines different statistical questions and techniques in the context of machine learning and clustering methods, the frailty models used in survival analysis, and other studies of statistics applied to diverse problems.

Nature evolves mainly in a statistical way. Different strategies, formulas, and conformations are continuously confronted in the natural processes. Some of them are selected and then the evolution continues with a new loop of confrontation for the next generation of phenomena and living beings. Failings are corrected without a previous program or design. The new options generated by different statistical and random scenarios lead to solutions for surviving the present conditions. This is the general panorama for all scrutiny levels of the life cycles.

Contents
1. A New Functional Clustering Method with Combined Dissimilarity Sources and Graphical Interpretation
2. Computational Statistics with Dummy Variables
3. Sparse Boosting Based Machine Learning Methods for High-Dimensional Data
4. Fast Computation of the EM Algorithm for Mixture Models
5. Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model
6. Modeling Heterogeneity Using Lindley Distribution
7. Network Meta-Analysis Using R for Diabetes Data
8. Variance Balanced Design
9. Estimation of Means of Two Quantitative Sensitive Variables Using Randomized Response Technique
10. Causality Relationship between Import, Export and Exim Bank Loans: Turkish Economy


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