學術產出-學位論文

題名 APC模型估計方法的模擬與實證研究
Simulation and empirical comparisons of estimation methods for the APC model
作者 歐長潤
Ou, Chang Jun
貢獻者 余清祥
Yue, Jack C.
歐長潤
Ou, Chang Jun
關鍵詞 APC模型
廣義線性模式
本質估計量
死亡率模型
電腦模擬
Age–Period–Cohort Model
Generalized Linear Models
Intrinsic Estimator
Mortality Rates Models
Simulation
日期 2008
上傳時間 18-九月-2009 20:10:50 (UTC+8)
摘要 20世紀以來,因為衛生醫療等因素的進步,各年齡死亡率均大幅下降,使得平均壽命大幅延長。壽命延長的效果近年逐漸顯現,其中的人口老化及其相關議題較受重視,因為人口老化已徹底改變國人的生活規劃,死亡率是否會繼續下降遂成為熱門的研究課題。描述死亡率變化的模型很多,近代發展的Age–Period–Cohort模型(簡稱APC模型),同時考慮年齡、年代與世代三個解釋變數,是近年廣受青睞的模型之一。這個模型將死亡率分成年齡、年代與世代三個效應,常用於流行病學領域,探討疾病、死亡率是否與年齡、年代、世代三者有關,但一般僅作為資料的大致描述,本研究將評估APC模型分析死亡率的可能性。
APC模型最大的問題在於不可甄別(Non–identification),即年齡、年代與世代三個變數存有共線性的問題,眾多的估計APC模型參數方法因應甄別問題而生。本研究預計比較七種較常見的APC模型估計方法,包括本質估計量(IE)、限制的廣義線性模型(cglim_age、cglim_period與cglim_cohort)、序列法ACP、序列法APC與自我迴歸模型(AR),以確定哪一種估計方法較為穩定,評估包括電腦模擬與實證分析兩部份。
電腦模擬部份比較各估計方法,衡量何者有較小的年齡別死亡率及APC參數的估計誤差;實證分析則考慮交叉分析,尋找用於死亡率預測的最佳估計方法。另外,也將以蒙地卡羅檢驗APC的模型假設,以確定這個模型的可行性。初步研究發現,以台灣死亡資料做為實證,本研究考量的估計方法在估計年齡別死亡率大致相當,只是在年齡–年代–世代這三者有不同的詮釋,且模型假設並非很符合。交叉分析上,Lee–Cater模型及其延展模型相對於APC模型有較小的預測誤差,整體顯示Lee–Cater 模型較佳。
Since the beginning of the 20th century, the human beings have been experiencing longer life expectancy and lower mortality rates, which can attributed to constant improvements of factors such as medical technology, economics, and environment. The prolonging life expectancy has dramatically changed the life planning and life style after the retirement. The change would be even more severe if the mortality rates have larger reduction, and thus the study of mortality become popular in recent years. Many methods were proposed to describe the change of mortality rates. Among all methods, the Age-Period-Cohort model (APC) is a popular method used in epidemiology to discuss the relation between diseases, mortality rate, age, period and cohort.
Non-identification (i.e. collinearity) is a serious problem for APC model, and many methods used in the procedure included estimation of parameter. In the first part of this paper, we use simulation compare and evaluate popular estimation methods of APC model, such as Intrinsic Estimator (IE), constrained of age, period and cohort in the Generalized Linear Model (c–glim), sequential method, and Auto-regression (AR) Model. The simulation methods considered include Monte-Carlo and cross validation. In addition, the morality data in Taiwan (Data sources: Ministry of Interior), are used to demonstrate the validity and model assumption of these methods. In the second part of this paper, we also apply similar research method to the Lee-Carter model and compare it to the APC model. We found Lee–Carter model have smaller prediction errors than APC models in the cross–validation.
參考文獻 中文部分
王郁萍(2006)。台灣地區死亡率APC模型之研究,國立政治大學統計研究所碩士論文。
李文宗(1994)。年齡--年代—世代分析方法新探,國立臺灣大學公共衛生學研究所博士論文。
郭雅雅(2005)。台灣地區服務業就業趨勢之年齡、年代及世代分析,國立政治大學統計研究所碩士論文。
郭雅婷(2007)。台灣地區女性勞動參與在生命歷程之變異-APC模型之應用,國立政治大學社會學研究所。
黃意萍、余清祥(2002)。台灣地區生育率模式的推估研究,人口學刊,25:145–171。
曾奕翔(2002)。台灣地區死亡率推估的實證方法之研究與相關年金問題之探討,國立政治大學統計研究所碩士論文。
余清祥、藍銘偉(2003)。台灣地區生育率模型之研究,人口學刊,27:105–131。
英文部分
Bell W R. (1997) Comparing and assessing time series methods for forecasting age–specific fertility and mortality rates. Journal of Official Statistics, 13(3): 279–303.
Cairns, A. J. G., Blake, D., and Dowd, K. (2009) A quantitative comparison of stochastic mortality models using data from England & Wales and the United States, North American Actuarial Journal. 13(1): 1–35.
Carstensen, B and Keiding, N. (2004) Age–period–cohort models: Statistical inference in the lexis diagram. Lecture notes, Department of Biostatistics, University of Copenhagen, http://www.biostat.ku.dk/~bxc/APC/notes.pdf
Carstensen, B and Keiding, N. (2005) Demography and epidemiology: Age–period–cohort models in the computer age, Department of Biostatistics, University of Copenhagen, http://www.pubhealth.ku.dk/bs/publikationer/rr-06-1.pdf.
Carter, L.R., and Lee, R.D. (1992) Modeling and forecasting US sex differentials in Mortality. International Journal of Forecasting, 8:393–411.
Christensen, R. (2002) Plane Answers to Complex Questions: The Theory of Linear Models, third edition, Springer–Verlag, New York.
Clayton, D. and Schifflers, E. (1987) Models for temporal variation in cancer rates I: Age–period and age–cohort models. Statistics in Medicine, 6:449–467.
Clayton, D. and Schifflers, E. (1987) Models for temporal variation in cancer rates II: Age–period–cohort models. Statistics in Medicine, 6:469–481.
Frost, W.H.(1939) The selection of mortality from tuberculosis in successive decades, American Journal of Hygiene (Section Age), 30:91–96.
Fu,W.J. (2000) Ridge Estimator in Singular Design with Application to Age–Period–Cohort Analysis of Disease Rates. Communications in Statistics-Theory and Method, 29:263–278.
Fu, W.J. Hall, P. and Rohan, T. (2004) Age–period–cohort analysis: structure of estimators, estimability, sensitivity and asymptotics, Technical report, Department of Epidemiology, Michigan State University.
Fu, W.J. and Hall, P. (2006) Asymptotic Properties of Estimators in Age-Period-Cohort Analysis. Statistics and Probability Letters, 76:1925–1929.
Fu,W.J. (2007). A Smoothing cohort model in Age–Period–Cohort Analysis with Applications to Homicide Arrest Rates and Lung Cancer Mortality Rates, http://www.msu.edu/~fuw/apc/apcsmthFinal.pdf
Gompertz, B. (1825) On the nature of the function expressive of the law of human mortality and on a mew mode of determining life contingentcies. Philosophical Transactions of the Royal Society of London, 115:513–585.
Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models, Chapman and Hall, New York.
Heligman, L. M. A. and Pollard, J. H. (1980) The age pattern of mortality. Journal of the Institute of Actuaries, 107(1): 49–82.
Holford TR. (1983) The estimation of age, period and cohort effects for vital rates. Biometrics, 39:311–324.
Holford TR. (2006) Approaches to fitting age–period–cohort models with unequal intervals. Statistics in Medicine, 25:977–993.
Kupper, L.L., Janis, J.M., Karmous, A. and Greenberg, B.G. (1985) Statistical age- period-cohort analysis: a review and critique. Journal of Chronic. Diseases, 38: 811–830.
Lee, W.C. and Lin, R.C. (1996) Autoregressive age period cohort models. Statistics in Medicine, 15:273–281.
Lee, R. D. and Carter L. R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87:659–675.
O’Bren, R.M. (2000) Age Period Cohort Characteristic Models. Social Science Research, 29, 123–139.
Osmond, C. and Gardner, M.J. (1982) Age, period and cohort models applied to cancer mortality rates. Statistics in Medicine, 1:245–259.
Renshaw, A. E., and Haberman, S. (2006) A cohort–based extension to the Lee-Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38: 556–570.
Robertson, C. Gandini, S. and Boyle, P. (1999) Age–Period–Cohort Models: A Comparative Study of Available Methodologies. Journal of Clinical Epidemiology, 52(6): 569–583.
Robertson , C. and Boyle, P. (1999) Age–peiod–cohort models of chronic disease rates I: modeling approaches. Statistics in Medicine, 17:1305–1323.
Robertson , C. and Boyle, P. (1999) Age–peiod–cohort models of chronic disease rates II: graphical approaches, Statistics in Medicine. 17:1325–1340.
Ryder, N. B. (1965) The cohort as a concept in the study of social change. American Sociological Review, 30:843–861.
Smith, H. (2008) Advances in Age–Period–Cohort Analysis, Sociological Methods and Research 36: 287–296.
Tarone RE, Chu KC. (1992) Implications of birth cohort patterns in interpreting trends in breast cancer rates. Journal of National Cancer Institute, 84:1402–1410.
Wilmoth, J. R. (1993) Computational Methods for Fitting and Extrapolating the Lee–Carter Model of Mortality Change. Technical report, Department of Demography, University of California, Berkeley.
Yang, Y., Fu, W.J. and Land, K. (2004) A methodological comparison of age–period–cohort models: the intrinsic estimator and conventional generalized linear models. Sociological Methodology, 34:75–110.
Yang, Y., Sam SW and Land, K. (2007) A Simulation Study of the Intrinsic Estimator
for Age–Period–Cohort Analysis, http://paa2008.princeton.edu/download.aspx?submissionId=80691
Yang, Y. and Land, K. (2008) Age–Period–Cohort Analysis of Repeated Cross-Section Surveys: Fixed or Random Effects? Sociological Methods and Research 36: 297–326.
Yang, Y. (2008) Trends in U.S. Adult Chronic Disease Mortality, 1960–1999: Age, Period, and Cohort Variations. Demography 45: 387–416.
Yang, Y., Sam SW, Fu, W.J. and Land, K. (2008) The Intrinsic Estimator for Age–Period–Cohort Analysis: What It is and How to Use it? American Journal of Sociology 113: 1697–1736.
描述 碩士
國立政治大學
統計研究所
96354007
97
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096354007
資料類型 thesis
dc.contributor.advisor 余清祥zh_TW
dc.contributor.advisor Yue, Jack C.en_US
dc.contributor.author (作者) 歐長潤zh_TW
dc.contributor.author (作者) Ou, Chang Junen_US
dc.creator (作者) 歐長潤zh_TW
dc.creator (作者) Ou, Chang Junen_US
dc.date (日期) 2008en_US
dc.date.accessioned 18-九月-2009 20:10:50 (UTC+8)-
dc.date.available 18-九月-2009 20:10:50 (UTC+8)-
dc.date.issued (上傳時間) 18-九月-2009 20:10:50 (UTC+8)-
dc.identifier (其他 識別碼) G0096354007en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/36928-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 96354007zh_TW
dc.description (描述) 97zh_TW
dc.description.abstract (摘要) 20世紀以來,因為衛生醫療等因素的進步,各年齡死亡率均大幅下降,使得平均壽命大幅延長。壽命延長的效果近年逐漸顯現,其中的人口老化及其相關議題較受重視,因為人口老化已徹底改變國人的生活規劃,死亡率是否會繼續下降遂成為熱門的研究課題。描述死亡率變化的模型很多,近代發展的Age–Period–Cohort模型(簡稱APC模型),同時考慮年齡、年代與世代三個解釋變數,是近年廣受青睞的模型之一。這個模型將死亡率分成年齡、年代與世代三個效應,常用於流行病學領域,探討疾病、死亡率是否與年齡、年代、世代三者有關,但一般僅作為資料的大致描述,本研究將評估APC模型分析死亡率的可能性。
APC模型最大的問題在於不可甄別(Non–identification),即年齡、年代與世代三個變數存有共線性的問題,眾多的估計APC模型參數方法因應甄別問題而生。本研究預計比較七種較常見的APC模型估計方法,包括本質估計量(IE)、限制的廣義線性模型(cglim_age、cglim_period與cglim_cohort)、序列法ACP、序列法APC與自我迴歸模型(AR),以確定哪一種估計方法較為穩定,評估包括電腦模擬與實證分析兩部份。
電腦模擬部份比較各估計方法,衡量何者有較小的年齡別死亡率及APC參數的估計誤差;實證分析則考慮交叉分析,尋找用於死亡率預測的最佳估計方法。另外,也將以蒙地卡羅檢驗APC的模型假設,以確定這個模型的可行性。初步研究發現,以台灣死亡資料做為實證,本研究考量的估計方法在估計年齡別死亡率大致相當,只是在年齡–年代–世代這三者有不同的詮釋,且模型假設並非很符合。交叉分析上,Lee–Cater模型及其延展模型相對於APC模型有較小的預測誤差,整體顯示Lee–Cater 模型較佳。
zh_TW
dc.description.abstract (摘要) Since the beginning of the 20th century, the human beings have been experiencing longer life expectancy and lower mortality rates, which can attributed to constant improvements of factors such as medical technology, economics, and environment. The prolonging life expectancy has dramatically changed the life planning and life style after the retirement. The change would be even more severe if the mortality rates have larger reduction, and thus the study of mortality become popular in recent years. Many methods were proposed to describe the change of mortality rates. Among all methods, the Age-Period-Cohort model (APC) is a popular method used in epidemiology to discuss the relation between diseases, mortality rate, age, period and cohort.
Non-identification (i.e. collinearity) is a serious problem for APC model, and many methods used in the procedure included estimation of parameter. In the first part of this paper, we use simulation compare and evaluate popular estimation methods of APC model, such as Intrinsic Estimator (IE), constrained of age, period and cohort in the Generalized Linear Model (c–glim), sequential method, and Auto-regression (AR) Model. The simulation methods considered include Monte-Carlo and cross validation. In addition, the morality data in Taiwan (Data sources: Ministry of Interior), are used to demonstrate the validity and model assumption of these methods. In the second part of this paper, we also apply similar research method to the Lee-Carter model and compare it to the APC model. We found Lee–Carter model have smaller prediction errors than APC models in the cross–validation.
en_US
dc.description.tableofcontents 第一章、前言------------------------------------------- 1
第一節、研究動機---------------------------------------- 1
第二節、研究目的---------------------------------------- 4
第二章、文獻探討---------------------------------------- 7
第一節、APC模型---------------------------------------- 7
第二節、估計APC模型參數方法------------------------------ 9
第三章、實證分析---------------------------------------- 16
第一節、資料收集---------------------------------------- 16
第二節、年齡、年代與世代的參數估計------------------------ 17
第三節、APC模型於死亡率估計------------------------------ 20
第四節、年代與世代的效應量測與比較------------------------ 21
第四章、檢查APC模型理論假設------------------------------ 24
第一節、殘差是否服從隨機常態分配-------------------------- 24
第二節、年齡組與年代組殘差是否服從隨機常態分配-------------- 26
第五章、評估不同APC模型估計方法--------------------------- 28
第一節、模型殘差與模型變異數------------------------------ 28
第二節、各年齡組與年代組的模型殘差與模型變異數-------------- 33
第三節、參數於空間座標的表現------------------------------ 38
第六章、APC模型與Lee–Carter模型及其延展模型--------------- 42
第一節、Lee–Carter模型於死亡率估計----------------------- 42
第二節、Lee–Carter模型的理論假設------------------------- 44
第三節、Lee-Carter延展模型於死亡率估計-------------------- 46
第四節、APC模型與Lee–Cater模型及其延展模型之交叉驗證------- 50
第七章、結論與討論--------------------------------------- 53
第一節、結論-------------------------------------------- 53
第二節、建議-------------------------------------------- 55
參考文獻------------------------------------------------ 56
附錄一、台灣地區死亡資料---------------------------------- 60
附錄二、台灣地區死亡資料的APC模型參數估計值與標準誤---------- 62
附錄三、台灣地區死亡資料的APC模型年代與世代參數配適值與殘差--- 67
附錄四、各方法之模擬殘差的機率密度函數圖-------------------- 73
zh_TW
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dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096354007en_US
dc.subject (關鍵詞) APC模型zh_TW
dc.subject (關鍵詞) 廣義線性模式zh_TW
dc.subject (關鍵詞) 本質估計量zh_TW
dc.subject (關鍵詞) 死亡率模型zh_TW
dc.subject (關鍵詞) 電腦模擬zh_TW
dc.subject (關鍵詞) Age–Period–Cohort Modelen_US
dc.subject (關鍵詞) Generalized Linear Modelsen_US
dc.subject (關鍵詞) Intrinsic Estimatoren_US
dc.subject (關鍵詞) Mortality Rates Modelsen_US
dc.subject (關鍵詞) Simulationen_US
dc.title (題名) APC模型估計方法的模擬與實證研究zh_TW
dc.title (題名) Simulation and empirical comparisons of estimation methods for the APC modelen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 中文部分zh_TW
dc.relation.reference (參考文獻) 王郁萍(2006)。台灣地區死亡率APC模型之研究,國立政治大學統計研究所碩士論文。zh_TW
dc.relation.reference (參考文獻) 李文宗(1994)。年齡--年代—世代分析方法新探,國立臺灣大學公共衛生學研究所博士論文。zh_TW
dc.relation.reference (參考文獻) 郭雅雅(2005)。台灣地區服務業就業趨勢之年齡、年代及世代分析,國立政治大學統計研究所碩士論文。zh_TW
dc.relation.reference (參考文獻) 郭雅婷(2007)。台灣地區女性勞動參與在生命歷程之變異-APC模型之應用,國立政治大學社會學研究所。zh_TW
dc.relation.reference (參考文獻) 黃意萍、余清祥(2002)。台灣地區生育率模式的推估研究,人口學刊,25:145–171。zh_TW
dc.relation.reference (參考文獻) 曾奕翔(2002)。台灣地區死亡率推估的實證方法之研究與相關年金問題之探討,國立政治大學統計研究所碩士論文。zh_TW
dc.relation.reference (參考文獻) 余清祥、藍銘偉(2003)。台灣地區生育率模型之研究,人口學刊,27:105–131。zh_TW
dc.relation.reference (參考文獻) 英文部分zh_TW
dc.relation.reference (參考文獻) Bell W R. (1997) Comparing and assessing time series methods for forecasting age–specific fertility and mortality rates. Journal of Official Statistics, 13(3): 279–303.zh_TW
dc.relation.reference (參考文獻) Cairns, A. J. G., Blake, D., and Dowd, K. (2009) A quantitative comparison of stochastic mortality models using data from England & Wales and the United States, North American Actuarial Journal. 13(1): 1–35.zh_TW
dc.relation.reference (參考文獻) Carstensen, B and Keiding, N. (2004) Age–period–cohort models: Statistical inference in the lexis diagram. Lecture notes, Department of Biostatistics, University of Copenhagen, http://www.biostat.ku.dk/~bxc/APC/notes.pdfzh_TW
dc.relation.reference (參考文獻) Carstensen, B and Keiding, N. (2005) Demography and epidemiology: Age–period–cohort models in the computer age, Department of Biostatistics, University of Copenhagen, http://www.pubhealth.ku.dk/bs/publikationer/rr-06-1.pdf.zh_TW
dc.relation.reference (參考文獻) Carter, L.R., and Lee, R.D. (1992) Modeling and forecasting US sex differentials in Mortality. International Journal of Forecasting, 8:393–411.zh_TW
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