死亡壓縮(Mortality Compression)意指死亡年齡更集中，是最近廣受注意的研究議題，和生存曲線矩形化（Rectangularization）關係密切，以統計分佈的角度描述，則是死亡年齡會逐漸退化到某個特定年齡。換言之，如果死亡壓縮和壽命有上限兩者都成立，以統計術語而言，代表壽命的期望值有上限、變異數會收斂，可藉由死亡年齡分配探討壽命變化。 本文希望以統計方法與資料品質等兩個面向探討死亡壓縮與延壽之間的關係。除了過去使用的無母數方法，如檢視各年度生命表上死亡分佈的最短區間（25%、50%及75%）與死亡人數最多的年齡（Modal Age）的變化，探討死亡壓縮與壽命是否有延長；另一方面，也將對死亡曲線作參數設定，觀察死亡年齡分佈的標準差變化。由於過往的研究多使用的生命表資料，本研究將比較使用生命表資料（死亡資料經過修勻）或原始死亡人數資料對結果的影響。 本研究藉由電腦模擬比較各種估計標準差方法的差異，包括Kannisto (2000) 提出的SD(M+)法與本文考量的非線性極值法(Nonlinear-Maximization)，衡量何者具有較小的均方誤差，並探討錯誤設定分配偵誤的敏感度；另外，本文可討論使用經過修勻的死亡率及原始死亡率對於估計結果的影響。除了電腦模擬，本研究也套入實際死亡資料（如臺灣、美國、…等國資料，資料來源：Human Mortality Database），檢視死亡壓縮是否存在。 / Mortality compression is one of the popular research issues in longevity risk. It means that the age-at-death would concentrate on a narrower range, and it is also related to the concept of rectangularization of survival curve. In terms of statistical distribution, mortality compression indicates that the age-at-death degenerates to a certain age, and it can be used to study changes of lifespan. If the lifespan has a limit, or mortality compression does exist, this suggests that the life expectancy has a limit and the variance of age-at-death would converge. In the study, we evaluate the mortality compression using the statistical methods and considering the issue of data quality. In addition to the nonparametric methods used in the previous studies, such as shortest confidence interval on the distribution of age-at-death and the modal age, we consider optimization methods for estimating the standard deviation of age-at-death distribution. In specific, we compare the SD(M+) proposed by Kannisto (2000) and the method of Nonlinear-Maximization, and check which method has a smaller MSE (Mean Squared Error). For the issue of data quality, we compare the estimation results of using mortality rates from life table data with those using the raw data. In addition to computer simulation, we consider the sensitivity analysis of age-at-death distribution, to evaluate the estimation method. Furthermore, based on the data from Human Mortality Database, we apply the method of Nonlinear-Maximization to life table data (i.e., graduated mortality rates) and raw data, and check if there are significant differences. The estimation results of empirical study are also used to evaluate if there is mortality compression and if there is a longevity limit.