Random sieve likelihood and general regression models
文献类型：期刊论文
| 作者 | Shen, XT; Shi, J; Wong, WH |
| 刊名 | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
 |
| 出版日期 | 1999-09-01 |
| 卷号 | 94期号:447页码:835-846 |
| 关键词 | empirical likelihood general regression model profile likelihood random sieve likelihood |
| ISSN号 | 0162-1459 |
| 英文摘要 | Consider a semiparametric regression model Y = f (theta, X, epsilon), where f is a known function, theta is an unknown vector, epsilon consists of a random error and possibly of some unobserved variables, and the distribution F(.) of (epsilon, X) is unspecified. This article introduces, in a general setting, new methodology for estimating theta and F(.). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirical likelihood but is more generally applicable. Four examples are discussed, including a quadratic model, high-dimensional semiparametric regression, a nonparametric random-effects model, and linear regression with right-censored data. Simulation results and asymptotic analysis support the utility and effectiveness of the proposed method. |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000082756400026 |
| 出版者 | AMER STATISTICAL ASSOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/14408]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Shen, XT |
| 作者单位 | 1.Ohio State Univ, Dept Stat, Columbus, OH 43210 USA 2.Acad Sinica, Inst Syst Sci, Beijing 100080, Peoples R China 3.Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA |
| 推荐引用方式 GB/T 7714 | Shen, XT,Shi, J,Wong, WH. Random sieve likelihood and general regression models[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,1999,94(447):835-846. |
| APA | Shen, XT,Shi, J,&Wong, WH.(1999).Random sieve likelihood and general regression models.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,94(447),835-846. |
| MLA | Shen, XT,et al."Random sieve likelihood and general regression models".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 94.447(1999):835-846. |
入库方式： OAI收割
来源：数学与系统科学研究院
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