收稿日期: 2014-11-21
网络出版日期: 2016-08-30
基金资助
国家自然科学基金资助项目(11371242)
Semi-parametric ordinal variable regression model
Received date: 2014-11-21
Online published: 2016-08-30
熊笛, 何幼桦 . 半参数顺序变量回归模型[J]. 上海大学学报(自然科学版), 2016 , 22(4) : 477 -485 . DOI: 10.3969/j.issn.1007-2861.2014.04.010
Based on a proportional odds model, the ordinal variable regression model is generalized, a semi-parametric ordinal regression model is established, and consistency of the estimators both in linear and nonlinear parts are proved in this paper. Simulation is conducted to analyze the correct rate and mean square error in the semi-parametric ordinal variable regression model with different sample sizes. The result shows that the semi-parametric ordinal regression model has high accuracy even with small samples. Compared to the number of observation points, the repeat number of experimental points has greater influence on accuracy. Calculation of the grain price warning problem shows that the semi-parametric ordinal regression model provides better extrapolation results than the proportional odds model.
[1] Duncan L R. Individual choice behavior: a theoretical analysis [M]. New York: John Wiley & Sons, 1959.
[2] Cox D R. The analysis of multivariate binary data [J]. Royal Statistical Society, 1972, 21(2): 113-120.
[3] McCullagh P. Regression models for ordinal data [J]. Journal of the Royal Statistical Society, 1980, 42(2): 109-142.
[4] Pettitt A N. Inference for the linear model using a likelihood based on ranks [J]. Journal of the Royal Statistical Society, 1982, 44(2): 234-243.
[5] Murphy S A, Rossini A J. Maximum likelihood estimation in the proportional odds model [J]. Journal of the American Statistical Association, 1997, 92(439): 968-976.
[6] Ibrahim J G, Chen M H, Maceachern S N. Bayesian variable selection for proportional hazards models [J]. The Canadian Journal of Statists, 1999, 27(4): 701-717.
[7] Lang J B. Bayesian ordinal and binary regression models with a parametric family of mixture links [J]. Computational Statistics & Data Analysis, 1999, 31(1): 59-87.
[8] Lam K F, Leung T L. Marginal likelihood estimation for proportional odds models with right censored data [J]. Lifetime Data Analysis, 2001, 7(1): 39-54.
[9] 冯国双, 陈景武, 周春莲. logistic 回归应用中容易忽视的几个问题[J]. 中华流行病学杂志, 2004, 25(6): 544-545.
[10] 赵宇东, 刘嵘, 刘延龄. 多元logistic 回归的共线性分析[J]. 中国卫生统计, 2001, 17(5): 259-261.
[11] 黄婷婷, 姜同敏. 基于比例危险-比例优势模型的加速寿命试验设计[J]. 北京航空航天大学学报, 2010, 36(5): 570-579.
[12] 唐俐玲, 翟晓红. 累积比数logit 模型在有序资料中的正确应用[J]. 徐州医学院学报, 2010, 30(9): 577-579.
[13] Engle R F, Granger C W J, Rice J, et al. Semiparametric estimates of the relationship between weather and electricity sales [J]. Journal of the American Statistical Association, 1986, 81(394): 310-320.
[14] Ruppert D, Wand M P. Multivariate locally weighted least squares regression [J]. The Annals of Statistics, 1994, 22(3): 1346-1370.
[15] 吴璇. 中国粮食价格预警系统研究[D]. 北京: 中国农业大学, 2003.
/
| 〈 |
|
〉 |