Modern Inference Based on Health-Related Markers: Biomarkers and Statistical Decision Making
Language: English
Published by Academic Press, 2024
- Softcover
- New
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- Title
- Modern Inference Based on Health-Related Markers: Biomarkers and Statistical Decision Making
- Publisher
- Academic Press
- Publication year
- 2024
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0128152478
- ISBN 13
- 9780128152478
Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. The book introduces correct and efficient testing mechanisms, including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications.
In addition, the book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies.
- Combines modern epidemiological and public health discoveries with cutting-edge biostatistical tools, including relevant software codes, offering one full package to meet the demand of practical investigators
- Includes the emerging topics from real health fields in order to display recent advances and trends in Biomarkers and associated Decision Making areas
- Written by researchers who are leaders of Epidemiological and Biostatistical fields, presenting up-to-date investigations related to the measuring health issues, emerging fields of biomarkers, designing health studies and their implementations, clinical trials and their practices and applications, different aspects of genetic markers
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Albert Vexler has belonged to the first cohort of investigators that proposed and discovered novel density-based empirical likelihood methodology. He has introduced the density-based empirical likelihood approach for creating nonparametric test statistics that efficiently approximate optimal parametric Neyman-Pearson statistics using minimum distribution assumptions on data. Recently, several statistical academic books referred the density-based empirical likelihood methodology to classical statistical procedures.
Dr. Jihnhee Yu obtained her PhD in Statistics from Texas A&M University in 2003. Currently Dr. Yu is Associate Professor at the State University of New York at Buffalo, Department of Biostatistics, and Director, Population Health and Health Observatory, School of Public Health and Health Profession, SUNY at Buffalo. She has authored and coauthored scientific papers published in several peer-reviewed journals throughout her career. Dr. Yu main research interests are clinical trials designs, parametric and nonparametric likehood approach.
SUNY Distinguished Professor, Dept. of Biostatistics, SPHHP, Assistant Director, IHI at University at Buffalo, Adjunct Professor, Computer Science
"About the title" may belong to another edition of this title.
Books Puddle
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