| LDR | | 01739nmm uu200385 4500 |
| 001 | | 000000330704 |
| 005 | | 20240805162003 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438099111 |
| 035 | |
▼a (MiAaPQ)AAI10901973 |
| 035 | |
▼a (MiAaPQ)OhioLINK:osu1503015935192212 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Bean, Andrew. |
| 245 | 10 |
▼a Transformations and Bayesian Estimation of Skewed and Heavy-Tailed Densities. |
| 260 | |
▼a [S.l.] :
▼b The Ohio State University.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 162 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Advisers: Xinyi Xu |
| 502 | 1 |
▼a Thesis (Ph.D.)--The Ohio State University, 2017. |
| 520 | |
▼a In data analysis applications characterized by large and possibly irregular data sets, nonparametric statistical techniques aim to ensure that, as the sample size grows, all unusual features of the data generating process can be captured. Good l |
| 520 | |
▼a This dissertation studies the Bayesian approach to the classic problem of nonparametric density estimation, in the presence of specific irregularities such as heavy tails and skew. The problem of estimating an unknown probability density is reco |
| 590 | |
▼a School code: 0168. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a The Ohio State University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0168 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000345
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |