Jiming Jiang – författare

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.

Features

Robust 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.

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.

Features

Robust 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.

Now in its second edition, this book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.

This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduate courses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.

Plant Centromere Biology is dedicated to plant centromere research. Chapters cover the structure of centromeres from several plant species including Arabidopsis thaliana, rice, maize, wheat and beet, while other sections cover several unique characteristics associated with plant centromeres, including classical and modern neocentromeres, centromere drive and centromere misdivision.  Additional chapters are dedicated to epigenetic modification and evolution of plant centromeres, and development and application of plant artificial chromosomes. 

Written by an international group of experts in the field, Plant Centromere Biology is a valuable handbook for all plant scientists working on plant genome research.  Beyond the bench, it can also serve as a helpful reference tool or textbook for upper level college classes on cytogenetics or genome analysis.

Plant Centromere Biology is dedicated to plant centromere research. Chapters cover the structure of centromeres from several plant species including Arabidopsis thaliana, rice, maize, wheat and beet, while other sections cover several unique characteristics associated with plant centromeres, including classical and modern neocentromeres, centromere drive and centromere misdivision.  Additional chapters are dedicated to epigenetic modification and evolution of plant centromeres, and development and application of plant artificial chromosomes. 

Written by an international group of experts in the field, Plant Centromere Biology is a valuable handbook for all plant scientists working on plant genome research.  Beyond the bench, it can also serve as a helpful reference tool or textbook for upper level college classes on cytogenetics or genome analysis.

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice. This monograph provides a comprehensive account of asymptotic analysis of mixed effects models.

The monograph is suitable for researchers and graduate students who wish to learn about asymptotic tools and research problems in mixed effects models. It may also be used as a reference book for a graduate-level course on mixed effects models, or asymptotic analysis.

Large sample techniques are fundamental to all fields of statistics. Mixed effects models, including linear mixed models, generalized linear mixed models, non-linear mixed effects models, and non-parametric mixed effects models are complex models, yet, these models are extensively used in practice. This monograph provides a comprehensive account of asymptotic analysis of mixed effects models.

The monograph is suitable for researchers and graduate students who wish to learn about asymptotic tools and research problems in mixed effects models. It may also be used as a reference book for a graduate-level course on mixed effects models, or asymptotic analysis.