Name
Abstract | ||
|---|---|---|
In survival analysis, it is of interest to appropriately select significant predictors. In this paper, we extend the AIC(C) selection procedure of Hurvich and Tsai to survival models to improve the traditional AIC for small sample sizes. A theoretical verification under a special case of the exponential distribution is provided. Simulation studies illustrate that the proposed method substantially outperforms its counterpart: AIC, in small samples, and competes it in moderate and large samples. Two real data sets are also analyzed. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.csda.2007.09.003 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
c selection procedure,small sample size,kullback–leibler information,bic,traditional aic,large sample,small sample,survival analysis,improved aic selection strategy,survival model,exponential distribution,aic,bioinformatics,biomedical research | Econometrics,Data set,Survival function,Survey sampling,Exponential distribution,Survival analysis,Statistics,Censoring (statistics),Mathematics,Sample size determination,Special case | Journal |
Volume | Issue | ISSN |
52 | 5 | 0167-9473 |
Citations | PageRank | References |
5 | 0.59 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Liang | 1 | 22 | 4.48 |
| Guohua Zou | 2 | 12 | 5.72 |