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Record Nr. |
UNINA9910484590903321 |
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Autore |
Latpate Raosaheb |
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Titolo |
Advanced Sampling Methods / / by Raosaheb Latpate, Jayant Kshirsagar, Vinod Kumar Gupta, Girish Chandra |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XVII, 301 p. 23 illus., 13 illus. in color.) |
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Disciplina |
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Soggetti |
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Statistics |
Mathematical statistics - Data processing |
Statistical Theory and Methods |
Bayesian Inference |
Statistics and Computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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-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. |
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Sommario/riassunto |
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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 |
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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. |
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