【摘要】 传统的Logistic回归参数估计(如极大似然估计)是在随机抽样的假设下做出的.但是,在基于选择(Choice-based)抽样条件下,传统的回归系数估计是有偏的.本文利用随机模拟的方法,比较了Logistic回归参数估计的3种方法,即先验概率法、加权法和传统的极大似然估计法,并列举了两种修改Logistic回归参数估计的方法.更多还原

【Abstract】 The traditional parameters estimation(maximum likelihood estimation for example) of logistic regression is based on mode-based inference which involves some hypotheses including infinite population,correct mode specification,etc.Under these hypotheses,if the sample is choice-based,the intercept is the only parameter estimate affected by a sample design that depends on the response variables.If these hypotheses are not satisfied as is often the case in practice,the inference method should be design-based.In this case,all the maximum likelihood estimations of logistic coefficients are biased if the sample is choice-based.The paper presents two methods to fit the logistic regression in choice-based samples and compares their effects on the computer.更多还原