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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56403完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪弘(Hung Hung) | |
| dc.contributor.author | Zhi-Yu Jou | en |
| dc.contributor.author | 周芷妤 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:26:58Z | - |
| dc.date.available | 2016-10-20 | |
| dc.date.copyright | 2014-10-20 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-14 | |
| dc.identifier.citation | [1] Buysse, D. J., Reynolds, C. F., and Monk, T. H. (1989). The Pittsburgh sleep
quality index: A new instrument for psychiatric practice and research. Psychiatry Research 28, 193-213. [2] B˚uˇzkov’a, P., Lumely, T., and Rice., K. (2011). Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions. Annals of Human Genetics 75, 36-45. [3] Henderson, H. V. and Searle, S. R. (1979). Vec and vech operators for matrices, with some uses in Jacobians and multivariate statistics. Canadian Journal of Statistics, 7, 65-81. [4] Hung, H. and Wang, C. C. (2012). Matrix variate logistic regression model with application to EEG data. Biostatistics 14, 189-202. [5] Lai, Y. C., Huang, M. C., Chen, H. C., Lu, M. K., Chiu, Y. H., Shen, W. W., Lu, R. B., and Kuo, P.H. (2014). Familiality and clinical outcomes of sleep disturbances in major depressive and bipolar disorders. Journal of Psychosomatic Research 76, 61-67. [6] Lin, X. (1997). Variance component testing in generalised linear models with random effects. Biometrika 84, 309-326. [7] Lin, X., Lee, S., Christiani, D. C., and Lin, X. (2013). Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics 14, 667-681. [8] Shapiro, A. (1986). Asymptotic theory of overparameterized structural models. Journal of American Statistical Association, 81, 142-149. [9] Zhou, H., Li, L., and Zhu, H. (2013). Tensor regression with applications in neuroimaging data analysis. Journal of the American Statistical Association 108, 540-552. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56403 | - |
| dc.description.abstract | 偵測基因與環境之交互作用為生物醫學研究中重要的議題之一。雖然廣義線性模式已被廣泛地使用於解決這類型的問題，但將會遭遇高維度的困難。在此研究中，我們利用矩陣結構並應用張量迴歸模式的技術來克服偵測基因與環境之交互作用所遇到的高維度困難。張量迴歸的優點之一為，在模式建構上使用較精簡的參數個數。因此，我們期望利用張量迴歸能更有效地且更有力地來偵測基因與環境之交互作用。利用張量迴歸偵測基因與環境之交互作用的另一個優點為可同時估計effect sizes。我們藉由模擬分析以及兩個資料集來評估本研究提出之方法的表現。 | zh_TW |
| dc.description.abstract | Testing the significance of gene×environment (G×E) interactions is an important issue in biomedical research. Although the generalized linear model has been widely applied in this problem, it will suffer the difficulty of high-dimensionality. In this study, we utilize the
matrix structure and apply the technique of tensor regression model to overcome the difficulty of high-dimensionality in detecting G×E. One advantage of tensor regression is the parsimony of parameters used. As a result, tensor regression is expected to be more efficient and powerful to detecting G×E. Another advantage of testing G×E by tensor regression is that the effect sizes can be estimated at the same time. We evaluate the performances of our methods through numerical studies and two data sets (the PSQI data and the EEG data). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:26:58Z (GMT). No. of bitstreams: 1 ntu-103-R01849031-1.pdf: 4534037 bytes, checksum: faf2b20af2a1f5994a7f0d7892e83999 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii 1 Introduction 1 2 Statistical Inference Procedure 4 2.1 Model specification and estimation . . . . . . . . . . . . . . . . . . . 4 2.2 Asymptotic properties . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Selection of r and λ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Detection of G×E 10 4 Simulation Studies 13 4.1 Simulation settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Simulation results of estimating G×E effects . . . . . . . . . . . . . . 14 4.3 Simulation results of detecting G×E . . . . . . . . . . . . . . . . . . 16 4.4 Data simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5 Data Analysis 20 5.1 PSQI data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 6 Conclusions 25 Reference 26 Appendices 28 | |
| dc.language.iso | en | |
| dc.subject | 過度參數化 | zh_TW |
| dc.subject | 基因×環境交互作用 | zh_TW |
| dc.subject | 張量迴歸 | zh_TW |
| dc.subject | 矩陣變項 | zh_TW |
| dc.subject | 假說檢定 | zh_TW |
| dc.subject | Overparameterization | en |
| dc.subject | Matrix covariate | en |
| dc.subject | Hypothesis testing | en |
| dc.subject | Gene×environment interactions | en |
| dc.subject | Tensor regression | en |
| dc.title | 利用張量迴歸偵測基因與環境之交互作用 | zh_TW |
| dc.title | Detection of Gene×Environment Interactions via
Tensor Regression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蕭朱杏(Chuhsing Kate Hsiao),郭柏秀(Po-Hsiu Kuo),陳素雲(Su-Yun Huang),程毅豪(Yi-Hau Chen) | |
| dc.subject.keyword | 基因×環境交互作用,張量迴歸,矩陣變項,假說檢定,過度參數化, | zh_TW |
| dc.subject.keyword | Gene×environment interactions,Tensor regression,Matrix covariate,Hypothesis testing,Overparameterization, | en |
| dc.relation.page | 30 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-14 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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| ntu-103-1.pdf 未授權公開取用 | 4.43 MB | Adobe PDF |
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