Topics on methodological and applied statistical inference / Tonio Di Battista, Elías Moreno, Walter Racugno, editors.
This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach,...
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Full Text (via Springer) |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2016.
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| Series: | Studies in theoretical and applied statistics.
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Table of Contents:
- Foreword; Preface; Contents; 1 Introducing Prior Information into the Forward Search for Regression; 1 Introduction; 2 Prior Information in the Linear Model from Fictitious Observations; 3 Algebra for the Bayesian Forward Search; 4 Example 1: Correct Prior Information; 5 Example 2: Incorrect Prior Information; 2 A Finite Mixture Latent Trajectory Model for Hirings and Separations in the Labor Market; 1 Introduction; 2 Data; 3 The Latent Trajectory Model; 3.1 Model Assumptions; 3.2 Estimation; 3.3 Model Selection; 4 Results; 5 Conclusions.
- 3 Outliers in Time Series: An Empirical Likelihood Approach1 Introduction; 2 The Empirical Likelihood; 3 Empirical Likelihood for Inference of Outliers in Time Series; 4 A Simulation Experiment and Real Time Series Study; 5 Conclusions; 4 Advanced Methods to Design Samples for Land Use/Land Cover Surveys; 1 Introduction; 2 Methodologies; 3 Empirical Evidence; 4 Concluding Remarks; 5 Heteroscedasticity, Multiple Populations and Outliers in Trade Data; 1 Introduction; 2 The Forward Search in Presence of Multiple Populations and Heteroscedasticity; 3 Forward Plot of the White Test.
- 4 Forward Search Monitoring with Harvey's Heteroscedastic Model5 Monthly Fair Prices; 6 Discussion; 6 How to Marry Robustness and Applied Statistics; 1 Introduction; 2 Which Method and How to Tune It?; 3 An Example of Monitoring; 4 Robustness Against What?; 4.1 Several Models: Clustering; 4.2 Which Model for the ̀Good' Data and How Many Outliers?; 5 Conclusion; 7 Logistic Quantile Regression to Model Cognitive Impairment in Sardinian Cancer Patients; 1 Introduction; 2 Logistic Quantile Regression; 3 Modeling Mini Mental State Examination; 4 Results and Discussion; 5 Conclusions and Remarks.
- 8 Bounding the Probability of Causation in Mediation Analysis1 Introduction; 2 Starting Point: Simple Analysis; 3 Additional Covariate Information; 3.1 Fully Observable; 3.2 Observable in Data Only; 4 Unobserved Confounding; 5 Mediation Analysis; 5.1 Example; 6 Discussion; 9 Analysis of Collaboration Structures Through Time: The Case of Technological Districts; 1 Introduction; 2 MFA for Network Data: Main Concepts; 3 The Collaboration Structure of the TD Under Analysis; 3.1 The Data; 3.2 Analysis of the Affiliation Structure over Time.
- 10 Bayesian Spatiotemporal Modeling of Urban Air Pollution Dynamics1 Introduction; 2 Data; 3 Spatiotemporal Modeling; 4 Results; 5 Conclusions; 11 Clustering Functional Data on Convex Function Spaces; 1 Introduction; 2 Functional Distances on Convex Function Spaces; 2.1 Functional k-means; 3 Application; 12 The Impact of Demographic Change on Sustainability of Emergency Departments; 1 Introduction and Background; 2 Data and Methods; 3 Results; 3.1 Impact on Demand; 3.2 Impact on Emergency Level; 3.3 Impact on Inappropriate Use of AED; 3.4 Change in Costs; 3.5 Sensitivity Analysis.