rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Recursive Partitioning and Applications
  name:
    name part: Zhang, Heping.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Singer, Burton H.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2010
    edition: Second.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 262 p. online resource.
  abstract: The routes to many important outcomes including diseases and ultimately death as well as financial credit consist of multiple complex pathways containing interrelated events and conditions. We have historically lacked effective methodologies for identifying these pathways and their non-linear and interacting features. This book focuses on recursive partitioning strategies as a response to the challenge of pathway characterization. A highlight of the second edition is the many worked examples, most of them from epidemiology, bioinformatics, molecular genetics, physiology, social demography, banking, and marketing. The statistical issues, conceptual and computational, are not only treated in detail in the context of important scientific questions, but also an array of substantively-driven judgments are explicitly integrated in the presentation of examples. Going considerably beyond the standard treatments of recursive partitioning that focus on pathway representations via single trees, this second edition has entirely new material devoted to forests from predictive and interpretive perspectives. For contexts where identification of factors contributing to outcomes is a central issue, both random and deterministic forest generation methods are introduced via examples in genetics and epidemiology. The trees in deterministic forests are reproducible and more easily interpretable than the components of random forests. Also new in the second edition is an extensive treatment of survival forests and post-market evaluation of treatment effectiveness. Heping Zhang is Professor of Public Health, Statistics, and Child Study, and director of the Collaborative Center for Statistics in Science, at Yale University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, a Myrto Lefkopoulou Distinguished Lecturer Awarded by Harvard School of Public Health, and a Medallion lecturer selected by the Institute of Mathematical Statistics. Burt…
  table of contents: A Practical Guide to Tree Construction -- Logistic Regression -- Classification Trees for a Binary Response -- Examples Using Tree-Based Analysis -- Random and Deterministic Forests -- Analysis of Censored Data: Examples -- Analysis of Censored Data: Concepts and Classical Methods -- Analysis of Censored Data: Survival Trees and Random Forests -- Regression Trees and Adaptive Splines for a Continuous Response -- Analysis of Longitudinal Data -- Analysis of Multiple Discrete Responses.
  note: by Heping Zhang, Burton H. Singer.
  subject:
    topic: Mathematics
  subject:
    topic: Health informatics
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Mathematics
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Health Informatics
  classification: QA273.A1-274.9
  classification: QA274-274.9
  classification: 519.2
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Series in Statistics, 0
  identifier: 9781441968241
  identifier: http://dx.doi.org/10.1007/978-1-4419-6824-1
  record info:
    record creation date: 100702
    record change date: 20151204160020.0
    record identifier: 978-1-4419-6824-1