Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life
- Indbinding:
- Paperback
- Sideantal:
- 555
- Udgivet:
- 23. oktober 2012
- Udgave:
- 12004
- Størrelse:
- 254x178x30 mm.
- Vægt:
- 1126 g.
- 8-11 hverdage.
- 6. december 2024
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Abonnementspris
- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life
Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields. Specific topics covered include:
* cancer prognosis using survival forests
* short-term health problems related to air pollution: analysis using semiparametric generalized additive models
* semiparametric models in the studies of aging and longevity
This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.
* cancer prognosis using survival forests
* short-term health problems related to air pollution: analysis using semiparametric generalized additive models
* semiparametric models in the studies of aging and longevity
This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.
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