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  • 學位論文

象徵性區間資料線性迴歸分析

Linear Regression Analysis for Symbolic Interval Data

指導教授 : 謝進見
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摘要


隨著科技時代的進步,我們蒐集資料的量愈來愈多且愈來愈複雜,這樣的變化,增加了我們使用傳統統計工具分析的困難度。在這篇論文中,我們探討象徵性區間資料線性迴歸模型的參數估計。我們考慮兩種不同的資料模型,並且提出不同的估計方法。之後,利用模擬實驗檢驗我們所提出的方法並且與一些現存的方法做比較。最後,我們利用我們所提出的方法分析兩筆真實資料並給予結論。

並列摘要


In the network technology era, the collected data are growing more and more complex, and become larger than before. It brings the difficulty to analyze by using the standard statistical tools. In this thesis, we focus on estimates of the linear regression parameters for symbolic interval data. We propose two approaches to estimate regression parameters for symbolic interval data under two different data models and compare our proposed approaches with the existing methods via simulations. Finally, we analyze two real datasets with the proposed methods for illustrations.

參考文獻


Billard, L. (2008). Sample covariance functions for complex quantitative data. Processing, World Conferences International Association of Statistical Computing 2008.
Carvalho, F.A.T., Neto, L. and Tenorio, C.P. (2004). A New Method to Fit a Linear Regression Model for Interval-valued Data. Lecture Notes in Computer Science, KI2004 Advances in Artical Inteligence. Springer-Verlag, 295-306.
Diday, E. (1987). Introduction a l'Approache Symbolique en Analyse des Donnees. Premieres Journees Symbolique - Numerique. CEREMADE, Universite Paris, 21-56.
Neto, L. and Carvalho, F.A.T. (2008). Centre and Range method for fitting a linear regression model to symbolic interval data. Computational Statistics and Data Analysis, 52, 1500-1515.
Bertrand, P. and Goupil, F. (2000). Descriptive statistics for symbolic data. Analysis of symbolic data: Exploratory methods for extracting statistical information from complex data. Springer-Verlag, 103-124.

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