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Multivariate Regression Modeling for Functional Data
並列摘要
We propose functional multivariate regression modeling, using Gaussian basis functions along with the technique of regularization. In order to evaluate the model estimated by the regularization method, we derive model selection criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations are conducted to investigate the efficiency of the proposed model. We also apply our modeling strategy to the analysis of spectrometric data.
被引用紀錄
賴政良（2009）。移除動態繫結的程式碼混亂化〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2009.00708
蔡杰穎（2014）。利用衝擊波管研究乙醛及異丁烷高溫熱解的反應動力學〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2014.00679
許夏銘（2014）。基於離散傅立葉轉換技術之LTE上行通道估計〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2014.00658
邱榮賓（2012）。二氧化鈦奈米晶粒表面處理對染料敏化太陽能電池效率影響之研究〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2012.00268
李佩芳（2011）。利用衝擊波管研究三重態亞甲基與氫氣和氧氣的高溫反應動力學〔碩士論文，國立交通大學〕。華藝線上圖書館。https://doi.org/10.6842/NCTU.2011.00387
延伸閱讀
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