| Judul | Bayesian Analysis of Failure Time Data Using P-Splines / Matthias Kaeding |
| Pengarang | Kaeding, Matthias |
| EDISI | 1 |
| Penerbitan | Abraham-Lincoln-Straße 46 65189, Wiesbaden, Germany : Springer Fachmedien Wiesbaden, 2015 |
| Deskripsi Fisik | 110 :ill |
| ISBN | 978-3-658-08393-9 |
| Subjek | PROBABILITY THEORY AND STOCHASTIC PROCESSES LABORATORY MEDICINE BIOINFORMATICS |
| Abstrak | Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. |
| Bentuk Karya | Tidak ada kode yang sesuai |
| Target Pembaca | Tidak ada kode yang sesuai |
| Lokasi Akses Online |
http://link.springer.com/openurl?genre=book&isbn=978-3-658-08392-2 |
| No Barcode | No. Panggil | Akses | Lokasi | Ketersediaan |
|---|---|---|---|---|
| 193315292 | 610 Kae b | Baca Online | Perpustakaan Pusat - Online Resources Ebook |
Tersedia |
| Tag | Ind1 | Ind2 | Isi |
| 001 | INLIS000000000159374 | ||
| 005 | 20250318100202 | ||
| 007 | ta | ||
| 008 | 250318################|##########|#|## | ||
| 020 | # | # | $a 978-3-658-08393-9 |
| 035 | # | # | $a 0010-0325000993 |
| 082 | # | # | $a 519.2 |
| 084 | # | # | $a 519.2 Kae b |
| 100 | 0 | # | $a Kaeding, Matthias |
| 245 | 1 | # | $a Bayesian Analysis of Failure Time Data Using P-Splines /$c Matthias Kaeding |
| 250 | # | # | $a 1 |
| 260 | # | # | $a Abraham-Lincoln-Straße 46 65189, Wiesbaden, Germany :$b Springer Fachmedien Wiesbaden,$c 2015 |
| 300 | # | # | $a 110 : $b ill |
| 520 | # | # | $a Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model. |
| 650 | # | # | $a BIOINFORMATICS |
| 650 | # | # | $a LABORATORY MEDICINE |
| 650 | # | # | $a PROBABILITY THEORY AND STOCHASTIC PROCESSES |
| 856 | # | # | $a http://link.springer.com/openurl?genre=book&isbn=978-3-658-08392-2 |
| 990 | # | # | $a 193315292 |
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