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[null,"1.","Record Nr.",null,"UNISANNIOMIL0432092"]
[null,null,"Autore",null,"Haykin, Simon S. <1931-    >"]
[null,null,"Titolo",null,"Chaotic dynamics of sea clutter / Simon Haykin, Sadasivan Puthusserypady"]
[null,null,"Pubbl/distr/stampa",null,"New York [etc.], : Wiley, c1999"]
[null,null,"ISBN",null,"0471252425"]
[null,null,"Descrizione fisica",null,"X, 217 p. : ill. ; 24 cm"]
[null,null,"Collana",null,"Adaptive and learning systems for signal processing, communications, and control"]
[null,null,"Disciplina",null,"621.3848"]
[null,null,"Soggetti",null,"Radar"]
[{"$nulls":4},"Onde marine"]
[null,null,"Collocazione",null,"SALA DING 621.3848                HAY.ch01SALA DING 621.3848                HAY.ch"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliografia: P. 201-212."]
[null,"2.","Record Nr.",null,"UNINA9911021978603321"]
[null,null,"Autore",null,"Dimotikalis Yiannis"]
[null,null,"Titolo",null,"Data Analysis and Related Applications, Volume 5 : Models, Methods and Techniques"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-40160-4"]
[{"$nulls":4},"1-394-40158-2"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (436 pages)"]
[null,null,"Collana",null,"ISTE Invoiced Series"]
[null,null,"Disciplina",null,"004"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright Page -- Contents -- Chapter 1. Modeling/Forecasting Patient Recruitment in Multicenter Clinical Trials Using Time-dependent Models -- 1.1. Introduction -- 1.2. Poisson-gamma model with time-dependent rates -- 1.2.1. The case of homogeneous rates -- 1.3. Non-homogeneous PG model -- 1.3.1. Estimation at the interim stage -- 1.3.2. Simulation of non-homogeneous PG model -- 1.4. Testing the recruitment rates for homogeneity -- 1.4.1. Poisson-type test -- 1.4.2. Criterion for testing hypothesis H0 -- 1.4.3. Poisson-gamma test -- 1.5. Implementations -- 1.6. Acknowledgment -- 1.7. References -- Chapter 2. Forecasting the Next Megacycle of the Economy -- 2.1. Introduction -- 2.2. 2024: the end of an economic megacycle -- 2.3. The role of technology in shaping the future -- 2.4. The economic consequences of the new cycle -- 2.4.1. Presenting past megacycles -- 2.4.2. The structure of economic megacycles -- 2.4.3. Technology as a catalyst for megacycles -- 2.4.4. Historical patterns of energy and economic growth -- 2.5. The future of economic megacycles -- 2.5.1. The 2024-2080 megacycle: a new era of exponential change and growth -- 2.5.2. Stagnation phase: 2024-2052 -- 2.5.3. Growth phase: 2052-2080 -- 2.5.4. Geopolitical and societal implications -- 2.5.5. Role of labor and automation -- 2.6. Conclusions -- 2.7. References -- Chapter 3. Modeling Functioning as a Determinant of Wellbeing: A Mediation Analysis -- 3.1. Introduction -- 3.2. Methods -- 3.2.1. Procedure and participants -- 3.2.2. Measures and item selection -- 3.2.3. Statistical analyses -- 3.3. Results -- 3.3.1. Univariate analysis -- 3.3.2. Bivariate "]
[{"$nulls":4},"analysis: correlation analysis -- 3.3.3. Multivariate analysis: mediation analysis -- 3.4. Conclusions -- 3.5. References -- 3.6. Appendix."]
[{"$nulls":4},"Chapter 4. Cross-Cultural Issues in Psychological Assessment: A Multistrategy Approach -- 4.1. Introduction -- 4.2. Suicide risk among university students: India and Italy in direct comparison -- 4.3. Merging techniques: A better way in cross-cultural studies? -- 4.4. Do different people respond in the same way to common items? -- 4.5. Conclusions -- 4.6. References -- Chapter 5. A Control Chart for Zero-Inflated Semi-Continuous Data -- 5.1. Introduction -- 5.1.1. Zero-inflated and hurdle models for count data -- 5.1.2. Inflated distributions for semi-continuous data -- 5.2. Zero-inflated Lomax distribution -- 5.2.1. The Lomax and the zero-inflated Lomax distributions -- 5.2.2. Maximum likelihood estimates -- 5.3. Shewhart control chart for monitoring zero-inflated Lomax data -- 5.4. Performance of the proposed control chart -- 5.5. Conclusions -- 5.6. Acknowledgments -- 5.7. References -- Chapter 6. Further Results on Location Invariant Estimation of the Weibull Tail Coefficient -- 6.1. Introduction -- 6.2. Hill and GMs EVI and WTC-estimators -- 6.2.1. Power-mean-of-exponent-p (PMp) and Holder's mean-of-order-p (MOp = Hp) EVI estimation -- 6.2.2. WTC estimation -- 6.3. Classes of PORT-GMs (PGMs) WTC-estimators -- 6.4. Monte Carlo simulation of the PORT-GPMp (PGPMp) WTC-estimators -- 6.5. Overall comments and open research topics -- 6.6. Acknowledgments -- 6.7. References -- Chapter 7. What Can we Learn from Malta? An Exploration of Gender Disparities in Education, Work and Money in Europe -- 7.1. Introduction -- 7.2. Gender gap: education, work and money -- 7.3. Gender Equality Index -- 7.4. Three-way data approach based on principal component analysis -- 7.5. Evidence from principal component analysis -- 7.6. Results of trajectory analysis -- 7.7. Conclusions -- 7.8. Acknowledgments -- 7.9. References."]
[{"$nulls":4},"Chapter 8. Financial Analysis of a Public Hospital: The Case of the Corfu General Hospital -- 8.1. Introduction -- 8.2. Materials and methods -- 8.3. Results -- 8.3.1. Liquidity ratios -- 8.3.2. Financial structure and viability ratios -- 8.3.3. Activity ratios -- 8.3.4. Profitability ratios -- 8.4. Discussion -- 8.5. References -- Chapter 9. EWMA Control Charts for Skewed Distributions -- 9.1. Introduction -- 9.2. Exponentially weighted moving average control charts -- 9.3. EMMA control charts for the non-normal process -- 9.3.1. The WVEWMA control chart -- 9.3.2. The WSDEWMA control chart -- 9.3.3. Newly proposed SCEWMA control chart -- 9.4. Real data -- 9.5. Simulation study -- 9.6. Simulation algorithm -- 9.7. Results and discussion -- 9.8. Conclusion -- 9.9. References -- Chapter 10. Assessing the Impact of Renewable Energy Sources on Energy Economics: A Non-Linear Regression Analysis of Hellenic Energy Exchange Market Clearing Prices -- 10.1. Introduction -- 10.2. Methodology -- 10.2.1. Spearman's rank -- 10.2.2. Sparse autoencoder -- 10.3. Results -- 10.4. Discussion -- 10.5. Conclusions -- 10.6. Acknowledgments -- 10.7. References -- Chapter 11. Enhancing Energy Market Stability: Comparative Analysis of Forecasting Techniques for Market Clearing Prices in the Day-Ahead Market -- 11.1. Introduction -- 11.2. Methodology -- 11.3. Results -- 11.4. Discussion -- 11.5. Conclusions -- 11.6. Acknowledgments -- 11.7. References -- Chapter 12. Using the Coxian Continuous-Time Hidden Markov Model to Analyze Lombardy Region Wards for Older Individuals -- 12.1. Introduction -- 12.2. Methodology -- 12.3. Data and results -- 12.3.1. Data -- 12.3.2. Results -- 12.4. Conclusions -- 12.5. Practice implications -- 12.6. Conflict of interest -- 12.7. References -- Chapter 13. Estimators for Extreme Value Index: Advancements in Tail Inference."]
[{"$nulls":4},"13.1. Introduction -- 13.2. Estimators for the tail parameters -- 13.2.1. The new class of estimators for the EVI -- 13.2.2. Asymptotic properties of the GPWM estimators -- 13.2.3. Estimating an extreme quantile -- 13.3. Monte Carlo simulation study of the GPWM estimators -- 13.3.1. Methodology -- 13.3.2. Results -- 13.4. Conclusion -- 13.5. Acknowledgments -- 13.6. References -- Chapter 14. Determinants of Students' Attitude Toward History: An Empirical Approach -- 14.1. Introduction -- 14.2. Previous research -- 14.2.1. Attitude toward history -- 14.2.2. Educational factors -- 14.2.3. Socioeconomic factors -- 14.3. Data and methods -- 14.3.1. Data -- 14.3.2. Empirical methodology -- 14.4. Results -- 14.5. Summary and conclusions -- 14.6. Appendices -- 14.6.1. Appendix A: the initial full questionnaire for the attitude survey toward history (EDIS) -- 14.6.2. Appendix B: the final questionnaire for the attitude survey toward history (EDIS) -- 14.6.3. Appendix C -- 14.7. References -- Chapter 15. Methodological Procedures for Assessing the Quality of Death Certificates Due to Unknown Causes -- 15.1. Introduction -- 15.2. Methods -- 15.2.1. First step: correction of underregistration of deaths (f) -- 15.2.2. Second step: redistribution of deaths due to ill-defined causes -- 15.2.3. Third step: redistribution of deaths due to non-specific causes (garbage codes) -- 15.3. Illustrative example -- 15.4. Conclusions -- 15.5. References -- Chapter 16. Health Status, Cancer and Pneumonia Death Rates in Europe: 2019-2022 -- 16.1. Introduction -- 16.2. Background -- 16.3. Methods -- 16.4. Results and discussion -- 16.4. Conclusions -- 16.5. References -- Chapter 17. A Bayesian Asymmetric Approach to Modeling Volatility on Portfolios with Many Assets -- 17.1. Introduction -- 17.2. Dynamic principal component analysis -- 17.3. Bayesian Student-t GJR(1,1) model."]
[{"$nulls":4},"17.4. Asymmetric modeling of a portfolio with many assets -- 17.5. Forecasting, predictive ability and risk estimation -- 17.6. Conclusion -- 17.7. References -- Chapter 18. Pandemic-Driven Innovations: Utilizing Online Learning and Big Data Analysis for Decision-Making in Educational Environments -- 18.1. Introduction -- 18.2. Literature review -- 18.2.1. Difficulties during the COVID-19 period -- 18.2.2. Effects of COVID-19 on education -- 18.2.3. Big data analysis in educational research -- 18.2.4. Related work -- 18.3. Methodology -- 18.4. Research questions -- 18.4.1. Dataset presentation -- 18.5. Conclusion -- 18.6. Suggestions for further research -- 18.7. References -- Chapter 19. Credit Card Fraud Detection with Machine Learning and Big Data Analytics: A PySpark Framework Implementation -- 19.1. Introduction -- 19.2. Literature review -- 19.2.1. Introduction to credit card fraud detection -- 19.2.2. The importance of detecting credit card fraud -- 19.2.3. Role of machine learning in improving decision-making processes in fraud detection -- 19.2.4. Automated pattern recognition -- 19.2.5. Predictive modeling -- 19.2.6. Dynamic risk scoring -- 19.2.7. Anomaly detection -- 19.2.8. Natural language processing (NLP) -- 19.2.9. Integration with existing systems -- 19.2.10. Credit card fraud detection: machine learning applications -- 19.2.11. Credit card fraud detection using Apache Spark -- 19.2.12. How can machine learning algorithms enhance decision quality in detecting fraud? -- 19.2.13. Improved detection accuracy -- 19.2.14. Real-time processing and analysis -- 19.2.15. Handling big data and complex variables -- 19.2.16. Adaptive learning for evolving threats -- 19.2.17. Cost efficiency through automation -- 19.2.18. Enhanced scalability -- 19.3. Materials and methods -- 19.3.1. Performance evaluation -- 19.4. Results."]
[{"$nulls":4},"19.4.1. Comparative analysis."]
[null,null,null,"Sommario/riassunto","This book is a collective work by several leading scientists, analysts, engineers, mathematicians and statisticians, who have been working at the forefront of data analysis and related applications, arising from data science, operations research, engineering, machine learning or statistics."]