| LDR | | 01782nmm uu200409 4500 |
| 001 | | 000000333407 |
| 005 | | 20240805172953 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438324862 |
| 035 | |
▼a (MiAaPQ)AAI10817004 |
| 035 | |
▼a (MiAaPQ)berkeley:17888 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 312 |
| 100 | 1 |
▼a Alexander, Monica. |
| 245 | 10 |
▼a Bayesian Methods for Mortality Estimation. |
| 260 | |
▼a [S.l.] :
▼b University of California, Berkeley.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 127 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: A. |
| 500 | |
▼a Adviser: Joshua R. Goldstein. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a The development of mortality models is important in order to reconstruct historical processes, understand current patterns and predict future trends. Mortality models are particularly useful when the available data are sparse, unreliable or inco |
| 520 | |
▼a This dissertation introduces Bayesian methods of mortality estimation in three contexts where the available data are imperfect. The first paper develops a method to estimate subnational mortality in situations with small populations and highly-v |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Demography. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0938 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Demography. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01A(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998312
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |