標題 99N18-家庭支持、遵醫囑行為與血糖控制之相關研究 -以某區域教學醫院為例 The Relationship between Family Support,Adherence and Blood Control:A Case of Regional Teaching Hospital.
姓名 陳香吟
指導教授 陳榮方 老師
畢業日期 2011/06
附件檔案 99N18Adobe PDF
參考連結
摘要 研究顯示:(1)糖尿病患者在性別、教育程度、每月家庭所得、婚姻狀況、有職業、與配偶同住者,在家庭支持量表有顯著差異。(2)糖尿病患者在性別、有無職業、每月家庭所得,在遵醫囑行為量表有顯著差異。(3)不同婚姻狀況,飯前血糖控制情形有顯著差異。患者在治療方式、血糖控制情形,其糖化血色素有顯著差異。(4)患者在遵醫囑行為「飲食控制情形」與血糖控制「飯前血糖值」有正向關係。(5)患者在家庭支持「家庭支持行為」、「感受性關懷」與血糖控制「飯前血糖值」有正向關係。(6)就血糖控制「飯前血糖值」而言,其最佳解釋力為家庭
參考文獻 中文部份
1. 江明珠、李政峰、廖四郎、徐守德等(2009),短期利率條件分配之尾部差異性檢定與風險值,《中山管理評論》17卷2 期 (17:2), p.517-554
2. 江明珠、李政峰與欉清全等(2011) “台灣不動產市場的下方風險-以台灣四個縣市為例”,《住宅學報》,第二十卷第二期,第1頁─24頁。
3. 台灣金融研訓院(2007),全球私人銀行及財富管理趨勢論壇。
4. 美林全球財富管理(Merrill Lynch Global Wealth Management)與凱捷顧問公司(Capgemini),2007及2009亞太區財富管理報告。
5. 耿順芬(2008),台灣金融產業財富管理市場發展契機與策略之探討,國立臺北大學國際財務金融碩士論文。
6. 陳哲瑜(2003),風險值在共同基金績效評估上之應用,國立中正大學企業管理研究所碩士論文。
7. 陳文雄(2008),財富管理,基金產業發展與兩者之合作關係:以台灣金融市場為例,國立中央大學財務金融學系碩士在職專班碩士論文。
8. 張素菱(2007),財富管理產業之實務探討,國立中央大學財務金融學系碩士在職專班碩士論文。
57
英文部份
1. Andersen, T. G., and J. Lund, 1997. Estimating Continuous Time Stochastic Volatility Models of the Short Term Interest Rate, Journal of Econometrics, 77: 343-377.
2. Bali, T. G., 2003. An Extreme Value Approach to Estimating Volatility and Value at Risk,Journal of Business, 76(1): 83-107.
3. Balkema, A. A., and L. de Haan, 1974. Residual Life Time at Great Age, Annals of Probability, 2:792-804.
4. Barunik J., & L. Vacha 2010 “Monte Carlo-based Tail Exponent Estimator,” Physica A. 389(21): 4863-4874.
5. Booth, G. G., J. P. Broussard, T. Martikainen, and V. Puttonen, 1997. Prudent Margin Levels in the Finnish Stock Index Futures Market, Management Science,43(8): 1177-1188.
6. Brenner, R. J., R. H. Harjes, and K. F. Kroner, 1996. Another Look at Models of the Short Term Interest Rate, Journal of Financial and Quantitative Analysis,31: 85-107.
7. Campbell, J. and L. Hentschell, 1992. No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns, Journal of Financial Economics,31: 281-318.
8. Cotter, J., 2001. Margin Exceedances for European Stock Index Futures using Extreme Value Theory, Journal of Banking and Finance, 25(8): 1475-1502.
58
9. Danielsson, J., and C. G. de Vries, 1997. Tail Index and Quantile Estimation with Very High Frequency Data, Journal of Empirical Finance, 4: 241-257.
10. Dowd, 1998, “Beyond Value at Risk”, John Wiley and Sons.Erasmus University Rotterdam.
11.de Haan, L., and S. I. Resnick, 1980. A Simple Asymptotic Estimate for the Index of a Stable Distribution, Journal of the Royal Statistical Society, series B, 42:83-87.
12. Embrechts, P., C. Klüppelberg, and T. Mikosch, 2003. Modelling Extremal Events for Insurance and Finance, Springer-Verlag, London.
13. Hill, B., 1975. A Simple General Approach to Inference About the Tail of a Distribution,Annuals of Mathematical Statistics, 3: 1163-1174.
14. Hsing, T. 1991 “On Tail Index Estimation using Dependent Data,” Annals of Statistics. 19(3): 1547-1569.
15. Jenkinson, A. F., 1955. The Frequency Distribution of the Annual Maximum (or Minimum) Values of Meteorological Elements, Quarterly Journal of the Royal Meteorology Society, 87: 145-158.
16. Jorion, 1996, “Value at Risk: the new benchmark for controlling market
risk”,Chicago: Irwin.
17. Jorion, November/December 1996, ”Risk2: Measuring the Risk in Value
at Risk,Financial Analysis Journal, pp47-56.
59
60
18. Kearns P., and A. Pagan, 1997. Estimating the Density Tail Index for Financial Time Series, The Review of Economics and Statistics, 79: 171-175.
19. Koedijk, K. G., F. G. J. A. Nissen, P. C. Schotman, and , C. C. P. Wolff, 1997. The dynamics of Short-term Interest Rate Volatility Reconsidered, European Finance Review, 1: 105-130.
20. Longin, F. M., 1999. Optimal Margin Level in Futures Markets: Extreme Price Movements, Journal of Futures Market, 19(2): 127-152.
21. Longin, M. F., 2000. From Value at Risk to Stress Testing: the Extreme Value Approach, Journal of Banking and Finance, 24: 1097-1130.
22. McNeil, A. J. and R. Frey, 2000. Estimation of Tail-related Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach, Journal of Empirical Finance, 7: 271-300.
23. Pickands, J., 1975. Statistical Inference using Extreme Order Statistics, Annals of Statistics, 3: 119-131.
24.Resnick, S. & C. Stărică 1996 “Testing the Covariance Stationarity of Heavy–tailed Time Series,” Journal Empirical Finance. 3(2): 211-248.
25. von Mises, R., 1936. La Distribution de la plus grande de n valeurs, American Mathematical Society Selected Papers, II: 271-294.