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題名 限制式下的潛在空間試題反應理論模型
Constrained Latent Space Item Response Model作者 林韋成 貢獻者 張育瑋
林韋成關鍵詞 貝氏估計
fused lasso
IRT 模型
Latent Space IRT 模型
local dependence
Bayesian estimation
fused lasso
IRT model
Latent Space IRT model
local dependence日期 2023 上傳時間 1-Dec-2023 13:59:39 (UTC+8) 參考文獻 1. Amthauer, R. (1953). Intelligenz-Struktur-Test (IST) [Intelligence Structure Test IST]. Göttingen, Germany: Hogrefe. 2. Amthauer, R. (1970). Intelligenz-Struktur-Test (IST-70) [Intelligence Structure Test IST70]. Göttingen, Germany: Hogrefe. 3. Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (2001). Intelligenz-StrukturTest 2000 R (I-S-T 2000 R) [Intelligence Structure Test IST 2000 R]. Göttingen, Germany: Hogrefe. 4. Bradlow, E. T., Wainer, H., & Wang, X. (1999). A Bayesian random effects model for testlets. Psychometrika, 64, 153-168. 5. Edwards, M. C., Houts, C. R., & Cai, L. (2018). A Diagnostic Procedure to Detect Departures From Local Independence in Item Response Theory Models. Psychological Methods, 23, 138-149. 6. Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741. 7. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40, 33–51. 8. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109. 9. Hoff, P., Raftery, A., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098. 10. Janssen, A. B. & Geiser, C. (2010). On the relationship between solution strategies in two mental rotation tasks. Learning and Individual Differences, 20, 473-478. 11. Jeon, M., Jin, I. H., Schweinberger, M., & Baugh, S. (2021). Mapping Unobserved Item–Respondent Interactions: A Latent Space Item Response Model with Interaction Map. Psychometrika, 86, 378–403. 12. Kyung, M., Gill, J., Ghosh, M., & Casella, G. (2010). Penalized Regression, Standard Errors, and Bayesian Lassos. Bayesian Analysis, 5, 369–412. 13. Lee, H. & Smith, W. Z. (2020). A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats. Educational and Psychological Measurement, 80, 578–603. 14. Liu, Y. & Maydeu-Olivares, A. (2012). Local Dependence Diagnostics in IRT Modeling of Binary Data. Educational and Psychological Measurement, 73, 254–274. 15. Morillo, D., Leenen, I., Abad, F. J., Hontangas, P. de la Torre, J., & Ponsoda, V. (2016). A dominance variant under the multi-unidimensional pairwise-preference framework: Model formulation and Markov Chain Monte Carlo estimation. Applied Psychological Measurement, 40, 500-516. 16. Putz-Osterloh, W. (1977). Über Problemlöseprozesse bei dem Test Würfelaufgaben aus dem Intelligenzstrukturtest IST und IST-70 von Amthauer [On solution processes in the test cube comparisons from Amthauer’s Intelligence Structure Test IST and IST-70]. Diagnostica, 23, 252−265. 17. Rasch, G. (1961). On general laws and meaning of measurement in psychology. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability (volume 4) (pp. 321–333). 18. Rost, J. (1990). Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis. Applied Psychological Measurement, 14, 271-282. 19. Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288. 20. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., & Knight, K. (2005). Sparsity and Smoothness via the Fused Lasso. Journal of the Royal Statistical Society, Series B, 67, 91-108. 21. Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. New York, NY: Cambridge University Press. 描述 碩士
國立政治大學
統計學系
110354024資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110354024 資料類型 thesis dc.contributor.advisor 張育瑋 zh_TW dc.contributor.author (Authors) 林韋成 zh_TW dc.creator (作者) 林韋成 zh_TW dc.date (日期) 2023 en_US dc.date.accessioned 1-Dec-2023 13:59:39 (UTC+8) - dc.date.available 1-Dec-2023 13:59:39 (UTC+8) - dc.date.issued (上傳時間) 1-Dec-2023 13:59:39 (UTC+8) - dc.identifier (Other Identifiers) G0110354024 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148545 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 110354024 zh_TW dc.description.tableofcontents 第一章 緒論 1 第二章 文獻的模型回顧 4 2.1 Rasch 模型 4 2.2 LS−IRT 模型 5 2.3 LS−IRT 模型的特例 9 第三章 FL−LS−IRT 模型 13 3.1 Fused Lasso 的介紹 13 3.2 FL−LS−IRT 模型介紹 14 3.3 FL−LS−IRT 模型的統計推論 16 3.3.1 先驗分配的設定 16 3.3.2 後驗分配與其統計推論流程 19 3.3.3 模型限制式與 m_a^2 及 n_a^2 之計算 24 第四章 模擬研究 30 4.1 模擬設定 30 4.2 模擬結果 31 第五章 實證分析 42 第六章 結論及建議 46 參考文獻 47 附錄 50 附錄A LS−IRT 模型在 a_p、b_j ∈ R^1 下的限制式推導 50 附錄B 引理證明 54 附錄C 對於 MH 演算法，參數接受率的推導 72 附錄D 超參數完全條件的推導過程 73 zh_TW dc.format.extent 1138664 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110354024 en_US dc.subject (關鍵詞) 貝氏估計 zh_TW dc.subject (關鍵詞) fused lasso zh_TW dc.subject (關鍵詞) IRT 模型 zh_TW dc.subject (關鍵詞) Latent Space IRT 模型 zh_TW dc.subject (關鍵詞) local dependence zh_TW dc.subject (關鍵詞) Bayesian estimation en_US dc.subject (關鍵詞) fused lasso en_US dc.subject (關鍵詞) IRT model en_US dc.subject (關鍵詞) Latent Space IRT model en_US dc.subject (關鍵詞) local dependence en_US dc.title (題名) 限制式下的潛在空間試題反應理論模型 zh_TW dc.title (題名) Constrained Latent Space Item Response Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Amthauer, R. (1953). Intelligenz-Struktur-Test (IST) [Intelligence Structure Test IST]. Göttingen, Germany: Hogrefe. 2. Amthauer, R. (1970). Intelligenz-Struktur-Test (IST-70) [Intelligence Structure Test IST70]. Göttingen, Germany: Hogrefe. 3. Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (2001). Intelligenz-StrukturTest 2000 R (I-S-T 2000 R) [Intelligence Structure Test IST 2000 R]. Göttingen, Germany: Hogrefe. 4. Bradlow, E. T., Wainer, H., & Wang, X. (1999). A Bayesian random effects model for testlets. Psychometrika, 64, 153-168. 5. Edwards, M. C., Houts, C. R., & Cai, L. (2018). A Diagnostic Procedure to Detect Departures From Local Independence in Item Response Theory Models. Psychological Methods, 23, 138-149. 6. Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741. 7. Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika, 40, 33–51. 8. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109. 9. Hoff, P., Raftery, A., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098. 10. Janssen, A. B. & Geiser, C. (2010). On the relationship between solution strategies in two mental rotation tasks. Learning and Individual Differences, 20, 473-478. 11. Jeon, M., Jin, I. H., Schweinberger, M., & Baugh, S. (2021). Mapping Unobserved Item–Respondent Interactions: A Latent Space Item Response Model with Interaction Map. Psychometrika, 86, 378–403. 12. Kyung, M., Gill, J., Ghosh, M., & Casella, G. (2010). Penalized Regression, Standard Errors, and Bayesian Lassos. Bayesian Analysis, 5, 369–412. 13. Lee, H. & Smith, W. Z. (2020). A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats. Educational and Psychological Measurement, 80, 578–603. 14. Liu, Y. & Maydeu-Olivares, A. (2012). Local Dependence Diagnostics in IRT Modeling of Binary Data. Educational and Psychological Measurement, 73, 254–274. 15. Morillo, D., Leenen, I., Abad, F. J., Hontangas, P. de la Torre, J., & Ponsoda, V. (2016). A dominance variant under the multi-unidimensional pairwise-preference framework: Model formulation and Markov Chain Monte Carlo estimation. Applied Psychological Measurement, 40, 500-516. 16. Putz-Osterloh, W. (1977). Über Problemlöseprozesse bei dem Test Würfelaufgaben aus dem Intelligenzstrukturtest IST und IST-70 von Amthauer [On solution processes in the test cube comparisons from Amthauer’s Intelligence Structure Test IST and IST-70]. Diagnostica, 23, 252−265. 17. Rasch, G. (1961). On general laws and meaning of measurement in psychology. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability (volume 4) (pp. 321–333). 18. Rost, J. (1990). Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis. Applied Psychological Measurement, 14, 271-282. 19. Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288. 20. Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., & Knight, K. (2005). Sparsity and Smoothness via the Fused Lasso. Journal of the Royal Statistical Society, Series B, 67, 91-108. 21. Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. New York, NY: Cambridge University Press. zh_TW
