研究領域

學歷

經歷

學術著作

期刊論文

1. Shih, Yu-Shan; Kung, Yi-Hung(2023).Tree-Structured Model with Unbiased Variable Selection and Interaction Detection for Ranking Data.MACHINE LEARNING AND KNOWLEDGE EXTRACTION, vol 5, pp. 448-459. (ESCI)

2.  Lin, Feng-Chang; Shih, Yu-Shan; Yu, Yuan-Bin; A nonparametric method for classification trees using grouped covariates. Biom. J. 65 (2023), no. 2, Paper No. 2100107, 13 pp.(SCIE)
3. G. Rau and Y.-S. Shih (2021). Evaluation of Cohen’s kappa and other measures of inter-rater agreement for genre analysis and other nominal data. Journal of English for Academic Purposes, vol 53, 101026.
4. N.-T. Liu, F.-C. Lin and Y.-S. Shih (2020). Count regression trees. Advances in Data Analysis and Classification. vol 14, pp. 5-27.
5. Y.-S. Shih and K.-H. Liu (2019). Regression trees for detecting preference patterns from rank data. Advances in Data Analysis and Classification. vol. 13, pp. 683-702.本人為第一作者、通訊作者.
6. Liu, Kuang-Hsun; Shih, Yu-Shan. (2016) Score-scale decision tree for paired comparison data. Statist. Sinica 26, no. 1, 429–444. 本人為通訊作者.
7. Hsin-Ju Chang, Yu-Shan Shih* and Tsung-Jen Su (2014, Jun). Split selection methods for regression tree on detecting regional economic convergence. Journal of the Chinese Statistical Association, 52, 180-208. NSC 100-2118-M-194-001. 本人為通訊作者.
8. Yi-Hung Kung, Chang-Ting Lin, Yu-Shan Shih* (2012, Sep). Split variable selection for tree modeling on rank data. Computational Statistics and Data Analysis, vol 56, pp. 2830-2836. (SCI). NSC 101-2118-M-194-001. 本人為通訊作者.
9. Shu-Fu Kuo, Yu-Shan Shih* (2012, Jul). Variable selection for functional density trees. Journal of Applied Statistics, vol 39, pp. 1387-1395. (SCI). NSC 96-2118-M-194-001-MY2. 本人為通訊作者.
10. Li-Ling Chuang, Y.-S. Shih* (2012, Feb). Approximated distributions of the weighted sum of correlated chi-squared random variables. Journal of Statistical Planning and Inference, vol 142, pp. 457-472. (SCI). 本人為通訊作者.
11. H. Kim, W.-Y. Loh, Y.-S. Shih, and P. Chaudhuri (2007).  Visualizable and interpretable regression models with good prediction power. IIE Transactions, vol 39, 565-579. (SCI). NSC 92-2118-M-194-001.
12. W.-C. Hsiao and Y.-S. Shih (2007). Splitting variable selection for multivariate regression trees. Statistics and Probability Letters, vol 77, 265-271. (SCI). NSC 94-2118-M-194-002.
13. T.-H. Lee and Y.-S. Shih (2006). Unbiased variable selection for classification trees with multivariate responses. Computational Statistics and Data Analysis, vol 51, 659-667. (SCI). NSC 94-2118-M-194-002.
14. Y.-S. Shih (2004, ). A Note on Split Selection Bias in Classification Trees. Computational Statistics and Data Analysis, vol 45, pp. 457-466. (SCI). NSC 90-2118-M-194-001.
15. Y.-S. Shih* and H.-W. Tasi (2004). Variable selection bias in regression trees with constant fits. Computational Statistics and Data Analysis, vol. 45, pp. 595-607. (SCI). NSC 91-2118-M-194-001.
16. D.-S. Liu*, Y.-S. Shih, C.-Y. Ni, D.-Y. Chiou, C.-L. Chung, and M.-L. Huang (2002, Nov). Influences of material source, wafer process and location on silicon die fracture strength. Experimental Techniques, vol. 26, pp. 29–35. (EI).
17. Y.-S. Shih (2001). Selecting the best splits classification trees with categorical variables. Statistics and Probability Letters, vol. 54, pp. 341-345. (SCI).
18. T.-S. Lim, W.-Y. Loh*, and Y.-S. Shih (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, vol. 40, pp. 203-228. (SCI). NSC 89-2118-M-194-005.
19. Yu-Shan Shih (1999). Families of splitting criteria for classification trees. Statistics and Computing , 9, 309-315. (SCI). 本人為第一作者、通訊作者.
20. Y.-S. Shih and W.-Y. Wu (1997). Studies on split selection methods for tree-structured regression. Journal of the Chinese Statistical Association, vol. 35, pp. 137-150.
21. W.-Y. Loh and Y.-S. Shih (1997, Jul). Split selection methods for classification trees . Statistica Sinica, vol 7, 815-840. (SCI).

專書論文

1. Bolt, D. M., Dowling, N. M., Shih, Y.-S., and Loh, W.-Y.. Using Blinder-Oaxaca decomposition to explore differential item functioning: Application to PISA 2009 reading. Test fairness in the new generation of large-scale assessment (ISBN: 1681238934). Charlotte, NC 28271, USA: Information Age Publisher. Jun, 2017: 161-183. http://www.infoagepub.com/products/Test-Fairness-in-the-New-Generation-of-Large-Scale-Assessment.

其他

1. Wei-Yin Loh, Yu-Shan Shih (2015). QUEST Classification Tree.

1. 條件推論與分類及迴歸樹.96-2118-M-194-001-MY2. Project Period: 2007/08/01-2009//07/31.

2. 多維分類及迴歸樹. 98-2118-M-194-003-MY2. Project Period: 2007/08/01-2009//07/31.

3. 具有轉折區域的空間模型. 100-2118-M-194-001-. Project Period: 2011/08/01-2012/07/31.

4. 秩序資料上的決策樹方法. 101-2118-M-194-001-. Project Period: 2012/08/01-2013/07/31.

5. 部分秩序和成對比較資料上的樹狀方法. 102-2118-M-194-002-. Project Period: 2013/08/01-2014/07/31.

6. 試題資料上決策樹的變數選取方法. 103-2118-M-194-001-. Project Period: 2014/08/01-2015/07/31.

7. 辨識試題資料 DIF 現象的決策樹方法. 104-2118-M-194-001-. Project Period: 2015/08/01-2016/07/31.

8. 布阿松廻歸樹中的分割選取和參數推論方法. 105-2118-M-194-001-. Project Period: 2016/08/01-2017/08/31.

9. 探索排序資料上喜好趨勢的迴歸樹方法. 106-2118-M-194-002-. Project Period: 2017/08/01-2018/09/30.

10. 部分治癒倖存資料上的迴歸樹方法. 107-2118-M-194-003-. Project Period: 2018/08/01-2019/09/30.

11. 分析多維計數資料的迴歸樹方法. 108-2118-M-194-003 -. Project Period: 2019/08/01-2020/09/30.

12. 離散時間事件資料上的樹狀方法. 109-2118-M-194-002 -. Project Period: 2020/08/01-2021/09/30.

13. 分析百分率資料的迴歸樹方法. 110-2118-M-194-001-. Project Period: 2021/08/01-2022/09/30.

14. 迴歸樹在疾病擴散模型的應用: 相關性資料的風險因子樹狀模型. 111-2118-M-194-003-. Project Period: 2022/08/01-2024/07/31

15. 離散存活樹狀模型. NSTC112-2118-M-194-001-.  Project Period:2023/08/01-2024/07/31