Robust Small Area Estimation by Jiang Jiming (17 results)

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Hardcover. Condition: new. Hardcover. In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term "small area" typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain.Keywords in SAE are borrowing strength. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no free lunch. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text.FeaturesA comprehensive account of methods, applications, as well as some open problems related to robust SAEMethods illustrated by worked examples and case studies using real dataDiscusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model predictionSupplemented with code and data via a websiteRobust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics. Intended to provide a nearly comprehensive account of methods, theory, applications, as well as open problems related to robust SAE, that the monograph will help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term 'small area' typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain.Keywords in SAE are 'borrowing strength'. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no 'free lunch'. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text.FeaturesA comprehensive account of methods, applications, as well as some open problems related to robust SAEMethods illustrated by worked examples and case studies using real dataDiscusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model predictionSupplemented with code and data via a websiteRobust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics. 276 pp. Englisch.

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Hardcover. Condition: new. Hardcover. In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term "small area" typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain.Keywords in SAE are borrowing strength. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no free lunch. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text.FeaturesA comprehensive account of methods, applications, as well as some open problems related to robust SAEMethods illustrated by worked examples and case studies using real dataDiscusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model predictionSupplemented with code and data via a websiteRobust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics. Intended to provide a nearly comprehensive account of methods, theory, applications, as well as open problems related to robust SAE, that the monograph will help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term 'small area' typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain.Keywords in SAE are 'borrowing strength'. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no 'free lunch'. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text.FeaturesA comprehensive account of methods, applications, as well as some open problems related to robust SAEMethods illustrated by worked examples and case studies using real dataDiscusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model predictionSupplemented with code and data via a websiteRobust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics.

Hardcover. Condition: new. Hardcover. In recent years there has been substantial and growing interest in small area estimation (SAE) that is largely driven by practical demands. Here, the term "small area" typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain.Keywords in SAE are borrowing strength. Because there are insufficient samples from the small areas to produce reliable direct estimates, statistical methods are sought to utilize other sources of information to do better than the direct estimates. A typical way of borrowing strength is via statistical modelling. On the other hand, there is no free lunch. Yes, one can do better by borrowing strength, but there is a cost. This is the main topic discussed in this text.FeaturesA comprehensive account of methods, applications, as well as some open problems related to robust SAEMethods illustrated by worked examples and case studies using real dataDiscusses some advanced topics including benchmarking, Bayesian approaches, machine learning methods, missing data, and classified mixed model predictionSupplemented with code and data via a websiteRobust Small Area Estimation: Methods, Applications, and Open Problems is primarily aimed at researchers and graduate students of statistics and data science and would also be suitable for geography and survey methodology researchers. The practical approach should help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. It could be used to teach a graduate-level course to students with a background in mathematical statistics. Intended to provide a nearly comprehensive account of methods, theory, applications, as well as open problems related to robust SAE, that the monograph will help persuade practitioners, such as those in government agencies, to more readily adopt robust SAE methods. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.