Quantitative Methods and Data Analysis in Applied Demography - Volume 2
Description
This second volume of two complements volume 1 by discussing models, risk and surveys in applied demography. Models and modeling cover a wide range of data analysis methods and techniques to cope with demographic data including principal components, clustering, GARCH models and dynamic correlation, multilevel models and stochastic insurance models including an R package for clustering as well. Surveys cope with the analysis of various data set arising in National and International context. Data from European Social Survey and National surveys are analyzed, as well as food waste generation analysis and social mobility in Europe along with post Covid-19 data sets.
By providing a methodology to cope with health and mortality problems in demography and society in Volume 1 and quantifying important health parameters in Volume 2, the books are a valuable guide for applied demographers, researchers, theoreticians, and practitioners from various disciplines and especiallyhealth scientists, statisticians, economists, and sociologists.
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Persons
Christos H. Skiadas, PhD, was the founder and director of the Data Analysis and Forecasting Laboratory at the Technical University of Crete and former Vice-Rector of the University. He is chair of the Demographics Workshop series, the Applied Stochastic Models and Data Analysis Conference series and the Chaotic Modeling and Simulation Conference series. He has published more than 80 papers, three monographs, and 24 books, including probability, statistics, data analysis and forecasting. His research interests include innovation diffusion modeling and forecasting, life table data modeling, healthy life expectancy estimates, and deterministic, stochastic, and chaotic modeling.
Charilaos Skiadas, PhD, is professor in mathematics and computer science at Hanover College, Indiana, USA. His research interests encompass a wide array of mathematical and computing topics, ranging from algebraic geometry to statistics and programming languages to data science and health statemodeling.
Content
Part I: Data Analysis.- Chapter 1. Introduction to Contents II.- Chapter 2. SEM and the analysis of Mexican poverty data.- Chapter 3. Equitable normal pension age adjusted to fertility and migration.- Chapter 4. Business density and economic growth in Portugal.- Chapter 5. A Rotated Principal Component Analysis for an Advanced Dimension Reduction Approach.- Chapter 6. A generalization of the k-means method for trends of time series.- Chapter 7. The influence of socio-economic variables on fertility in selected European countries.- Chapter 8. A Topological Clustering on Evolutionary Data.- Chapter 9. fdaMocca: An R package for model-based clustering for functional data with covariates.- Chapter 10. Factors explaining perceptions towards Information and Communication Technologies with data drawn from the European Social Survey.- Part II: Models.- Chapter 11. Assessing pollution risk using asymmetric GARCH Models and Dynamic Correlation.- Chapter 12. The influence of the asymptomatic transmission on the number of symtomatic cases within a modified ISR model.- Chapter 13. Stochastic Insurance Models with Investment and Reinsurance.- Chapter 14. Prosocial Behaviour of Students in Maltese Schools: A Multilevel Model.- Chapter 15. Data Analysis of Discrete-Valued Models for Genetic Sequences.- Part III: Risk.- Chapter 16. A Note on the Convergence of Euler Contributions, Depending on the Underlying Risk Measure.- Chapter 17. Approximate Formula for Adjustment Coefficient of a Non-linear Risk Model with Weibull Claims.- Chapter 18. Comparative Performance Analysis of YOLOv4 and YOLOv5 Algorithms on Dangerous Objects.- Part IV: Surveys.- Chapter 19. Intergenerational social mobility in Europe: Findings from the European Social Survey.- Chapter 20. Exploring measurement invariance: Political trust in national institutions across social groups.- Chapter 21. A statistical comparative household food waste generation analysis.- Chapter 22. Modeling Religion Variables on Self-perceived Social Class: Evidence from the 7th Wave of the World Values Survey, 2017-2020.