Advanced sampling methods / / Raosaheb Latpate [and three others]

• Record Nr.: UNISA996466397403316
• Autore: Latpate Raosaheb
• Pubbl/distr/stampa: Singapore : , : Springer, , [2021]; ©2021
• ISBN: 981-16-0622-6
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (XVII, 301 p. 23 illus., 13 illus. in color.)
• Disciplina: 519.52
• Soggetti: Sampling (Statistics); R (Computer program language); Mostreig (Estadística); Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: -1. Introduction -- 2. Simple Random Sampling -- 3. Stratied Random Sampling -- 4. Cluster Sampling -- 5. Double Sampling -- 6. Probability Proportional to Size Sampling -- 7. Systematic Sampling -- 8. Resampling Techniques -- 9. Adaptive Cluster Sampling -- 10. Two-Stage Adaptive Cluster Sampling -- 11. Adaptive Cluster Double Sampling -- 12. Inverse Adaptive Cluster Sampling -- 13. Two Stage Inverse Adaptive Cluster Sampling -- 14. Stratified Inverse Adaptive Cluster Sampling -- 15. Negative Adaptive Cluster Sampling -- 16. Negative Adaptive Cluster Double Sampling -- 17. Two- Stage Negative Adaptive Cluster Sampling -- 18. Balanced and Unbalanced Ranked Set Sampling -- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference -- 20. Important Versions of Ranked Set Sampling -- 21. Sampling Errors.
• Sommario/riassunto: This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary ; variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.