請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33319完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江金倉 | |
| dc.contributor.author | Xiao-Wei Lin | en |
| dc.contributor.author | 林曉薇 | zh_TW |
| dc.date.accessioned | 2021-06-13T04:34:26Z | - |
| dc.date.available | 2006-07-21 | |
| dc.date.copyright | 2006-07-21 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-19 | |
| dc.identifier.citation | [1]Cai, Z. and Sun, Y. (2003). Local linear estimation for time-dependent coefficients
in Cox's regression models. Scandinavian Journal of Statistics. 30, 93-111. [2]Casella, G. and Robert, C. P. (1996). Rao-Blackwellisation of sampling schemes. Biometrika.83, 81-94. [3]Chiang, C. T., Rice, J. A., and Wu, C. O. (2000). Smoothing spline estimation for varying coefficient models with repeatedly measured dependent variable. Journal of the American Statistical Association.96, 605-619. [4]Cook, R. J., and Lawless, J. F. (1997). Marginal analysis of recurrent events and a terminating event. Statistics in Medicine. 16, 911-924. [5]Cook, R. J., and Lawless, J. F. (1997). Analysis of repeated events. Statistical Methods in Medical Research. 11, 141-166. [6]Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete observations. Journal of the Royal Statistical Society. B39, 1-38. [7]Fan, J. Q. and Zhang, J. T. (2000). Functional linear models for longitudinal data. Journal of the Royal Statistical Society.B62, 303-322. [8]Ghosh, D. and Lin, D. Y. (2000). Nonparametric analysis of recurrent and death. Biometrics.56, 554-562. [9]Ghosh, D. and Lin, D. Y. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica. 12, 663-688. [10]Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association.87, 942-951. [11]Gruttola, D. V. and Tu, X. M. (1994). Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics. 50, 1003-1014. [12]Hammerlin, G. and Hoffmann, K. (1991).Numerical Mathematics.Springer-Verlag, New York. [13]Henderson, R., Diggle, P., and Dobson, A. (2000). Joint modeling of longitudinal measurements and event time data. Biostatistics. 4, 465-480. [14]Hoover, D. R., Rice, J. A., Wu, C. O. and Yang, L. P. (1998). Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika. 85, 809-822. [15]Huang J. Z., Wu C. O., and Zhuo L. (2002). Varying-coefficient models and basis function approximations for the analysis of repeated measurements. Biometrika. 89, 111-128. [16]Huang, C. Y. and Wang, M. C. (2004). Joint modeling and estimation for recurrent event processes and failure time data. Journal of the American Statistical Association. 99, 1153-1165. [17]Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics. 38, 963-974. [18]Lancaster, A. and Intrator, O. (1998). Panel data with survival: Hospitalization of HIV-positive patients. Journal of the American Statistical Association. 93, 46-53. [19]Li, Q. and Lagakos, S. (1997). Use of the WEi-Lin-Weissfeld method for the analysis of a recurring and a terminating event. Statistics in Medicine. 16, 925-940. [20]Lin, D. Y., Feure, E. J., Etzioni, R. and Wax, Y. (1997). Estimating medical costs from incomplete follow-up data. Bimetrics. 53, 419-434. [21]Lin, D. Y., Wei, L. J., Yang, I. and Ying, Z. (2000). Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society. B62, 711-730. [22]Liu, L., Wolfe, R. A. and Huang, X. (2004). Shared frailty models for recurrent events and a terminal event. Bimoetrics. 60, 747-756. [23]Martinussen, T. and Scheike, T. H. (2002). A flexible additive multiplicative hazard model. Biometrika. 89, 283-298. [24]Murphy, S. and Sen, P. (1991). Time-dependent coefficients in a Cox-type regression model. Stochastic Processes an Their Applications. 39, 153-180. [25]Nielsen, J. D. and Dean, C. B. (2005). Regression splines in the quasi-likelihood analysis of recurrent event data. Journal of Statistical Planning and Inference. 134, 521-535. [26]Nurnberger, G. (1989). Approximation by Spline. Springer-Verlag, New York. [27]Sammel, M. D. and Ryan, L. M. (1996). Latent variable models with fixed effects. Biometrics. 52, 650-663. [28]Tian, L., Zucker, D., and Wei, L. J. (2005). On the Cox model with time-varying regression coefficients. Journal of the American Statistical Association. 100, 172-183. [29]Tesng, Y. K., Hsieh, F. and Wang, J. L. (2005). Joint modelling of accelerated failure time and longitudinal data. Biometrika. 92, 587-603. [30]Tsiatis, A. A. (1981). A large sample study of Cox's regression model. The Annals of Statistics. 9, 93-108. [31]Tsiatis, A. A., DeGruttola, V., and Wulfsohn, M. S. (1995). Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association. 90, 27-37. [32]Tsiatis, A. A. and Davidian, M. (2001). A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika. 88, 447-458. [33]Tsiatis, A. A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: an overiew. Statica Sinica. 14, 809-834. [34]Wang, M. C., Qin, J. and Chiang, C. T. (2001). Analyzing recurrent event data with informative censoring. Journal of the American Statistical Association. 96, 1057-1065. [35]Winnett, A. and Sasieni, P. (2003). Iterated residuals and time-varying covariate effect in Cox regression. Journal of the Royal Statistical Society. B62, 473-488. [36]Wu, C. O., Chiang, C. T., and Hoover, D. R. (1998). Asymptotic confidence regions for kernel smoothing of a varying coefficient model with longitudinal data. Journal of the American Statistical Association. 93, 1388-1402. [37]Wu, C. F. J. (1983). On the convergence of the EM algorithm. The Annals of Statistics.11, 95-103. [38]Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics. 53, 330-339. [39]Zuuker D. M. and Karr A. F. (1990). Nonparametric survival analysis with time-dependent cvariates: a penalized partial likelihood approach. The Annals of Statistics. 18, 329-353. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33319 | - |
| dc.description.abstract | 本論文主要針對時間函數之反應值，重覆量測時間及存活時間建立一變異係數潛藏因子模型。藉由半參數化之潛藏因子建立這些過程之內部相關，彼此之相關性以及非齊一性質。在長期追蹤以及存活資料結構下，我們利用基底展式估計值作為參數函數之估計。更進一步，我們推導所提出估計函數之大樣本性質，並藉助模擬檢視估計式之有限樣本性質。 | zh_TW |
| dc.description.abstract | In this thesis, a joint latent varying-coefficient model is proposed to establish the relationship among processes of time-dependent response, measurement time or recurrent event time, and terminal time. The dependence within and among these processes and the heterogeneity on each process are modelled through partially nonparametric latent variables. Based on the longitudinal and survival time data, an estimation approach is proposed for the
parameter functions in the considered joint latent model. In our estimation procedure, each parameter function is first substituted by the corresponding basis function expansions, and, hence, the approximated likelihood function is induced. By implementing the expectation and maximization (EM) algorithm or directly using the integration technique, the estimates of parameters in the basis function expansions are obtained. It is naturally to use the estimated functions of basis function expansions as the estimators of the corresponding parameter functions. In this thesis, the asymptotic and finite sample properties of the estimated functions are also derived and examined through a Monte-Carlo simulation, respectively. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T04:34:26Z (GMT). No. of bitstreams: 1 ntu-95-R93221014-1.pdf: 745875 bytes, checksum: 269a033cbb334b205a0716138425e802 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Table of Contents ii
List of Tables iii List of figures iv Acknowledgements vi Abstract vii 摘要 viii 1 Introduction 1 2 Joint Latent Model and Estimation 4 2.1 Model 5 2.2 Estimation 8 3 Asymptotic Properties 10 4 Numerical Study 20 5 Further Study 30 Bibliography 31 | |
| dc.language.iso | en | |
| dc.subject | 存活時間 | zh_TW |
| dc.subject | 重覆量測時間 | zh_TW |
| dc.subject | 潛藏因子 | zh_TW |
| dc.subject | 基底展式 | zh_TW |
| dc.subject | 長期追蹤資料 | zh_TW |
| dc.subject | basis function expansion | en |
| dc.subject | varying-coefficient | en |
| dc.subject | longitudinal data | en |
| dc.subject | recurremt evemt | en |
| dc.subject | latent variable | en |
| dc.subject | measurement times | en |
| dc.subject | terminal time | en |
| dc.subject | B-spline | en |
| dc.title | 長期追蹤,重覆量測時間及存活時間之潛藏因子結合模型 | zh_TW |
| dc.title | A Joint Latent Model for Time-dependent Response, Measurement Time, and Terminal Time Processes | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃冠華,陳宏 | |
| dc.subject.keyword | 基底展式,長期追蹤資料,重覆量測時間,存活時間,潛藏因子, | zh_TW |
| dc.subject.keyword | basis function expansion,B-spline,terminal time,measurement times,latent variable,recurremt evemt,longitudinal data,varying-coefficient, | en |
| dc.relation.page | 34 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-20 | |
| dc.contributor.author-college | 理學院 | zh_TW |
| dc.contributor.author-dept | 數學研究所 | zh_TW |
| 顯示於系所單位： | 數學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-95-1.pdf 未授權公開取用 | 728.39 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。