Wang D, Murphy M
London School of Economics, Great Britain.
Soc Biol. 1998 Spring-Summer;45(1-2):21-38. doi: 10.1080/19485565.1998.9988962.
To elucidate the nature of the relationship between infant mortality in China and a variety of covariates using data from the 2/1000 Chinese Fertility Survey, we use a logistic regression model where the covariates are transformed with the help of Alternating Conditional Expectation (ACE) algorithm. This approach is used to overcome the general problem in multivariate regression analysis of coding the independent variables so that relationship between independent variables and response variables is best described, rather than coding such variables in an arbitrary way. The study demonstrates the procedures and usefulness of the ACE guided transformation in multivariate analysis. The transformed covariates are then used to estimate the effects of a series of socioeconomic and demographic factors collected in the study of infant death in China. The study shows that after appropriate transformations, all the demographic and socioeconomic variables selected have statistically significant and direct influence on infant death.
为利用中国千分之二生育率调查的数据阐明中国婴儿死亡率与各种协变量之间关系的本质，我们使用逻辑回归模型，其中协变量借助交替条件期望（ACE）算法进行变换。这种方法用于克服多元回归分析中对自变量进行编码的一般问题，以便最好地描述自变量与响应变量之间的关系，而不是以任意方式对这些变量进行编码。该研究展示了ACE引导变换在多元分析中的程序和实用性。然后，将变换后的协变量用于估计在中国婴儿死亡研究中收集的一系列社会经济和人口因素的影响。研究表明，经过适当变换后，所选择的所有人口和社会经济变量对婴儿死亡都具有统计学上显著的直接影响。
