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題名:多變量迴歸模型建構程序之研究
作者:鄒幼涵
作者(外文):Yu-Han Tsou
校院名稱:輔仁大學
系所名稱:商學研究所
指導教授:黃登源、陳瑞照
學位類別:博士
出版日期:2008
主題關鍵詞:邏輯斯迴歸模型基因演算法赤池資訊量模型診斷變數選擇財務金融資料Akaike information criterionfinancial statistic datagenetic algorithmlogistic regressionmodel diagnosisvariables selection
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本文針對多變量迴歸模型建構程序之研究,包括基本假設、參數估計、模型診斷與變數選取,就不同資料型態的模型,提出基本假設評估、參數估計方法與變數選擇準則之程序。本研究提出二階段建構多變量迴歸模型分析程序,第一階段採用轉換方法,將多變量模型轉換成直交單一模型體系,並證明單一模型誤差加總與多變量模型相同,且相互獨立,第二階段再進行參數估計與模型診斷評估。另邏輯斯迴歸模型建構程序,為解決大量參數估計計算之有效性,採用基因演算法,以傳統最大概似法所得之參數估計值作為起始值,利用突變過程,變動起始值,盡量避免產生局部最小值,使計算更有效率。在模型配適與診斷方面,分別就不同資料型態所建構的模型,以最小偏差量或最小AIC值為判斷準則,選取較適合的變數來建立迴歸模型。本研究以實際財務金融資料及觀光調查資料來檢驗不同資料型態的模型建構程序。
The paper is the study of modeling procedures for multivariate regression models. Based on the different model of data type, it aims to propose the procedure of basic assumption evaluation, method of parameter estimation and the criteria of variable selections.
The study proposes the two-stage modeling procedures for multivariate regression models. The first stage uses a transform method to change the multivariate model to a separate univariate model system, and proves the error sum of squares of the univariate model system equals to the multivariate regression model and each of the univariate model is mutually independent. The parameter estimation and model diagnosis are then used in the second stage. To solve a great deal of the calculation of parameter estimation, the modeling procedure of logistic regression model employs the Genetic Algorithm to find the initial value through the general maximum likelihood method. In the process of mutation, an initial value is changed to avoid of the local solution. This improves the efficiency of the calculations. In terms of goodness of fit and diagnosis, Huang(1996)the minimal bias or Akaike(1973)minimal AIC value were taken as variables selection criteria to fit different models of data type to set up the parsimony multivariate regression model.
The study verified different modeling procedure of data type according to the data of practical finance and tourism investigation.
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