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对含有非随机缺失数据的潜变量增长模型,为了考察基于不同假设的缺失数据处理方法:极大似然(ML)方法与DiggleKenward选择模型的优劣,通过Monte Carlo模拟研究,比较两种方法对模型中增长参数估计精度及其标准误估计的差异,并考虑样本量、非随机缺失比例和随机缺失比例的影响。结果表明,符合前提假设的Diggle-Kenward选择模型的参数估计精度普遍高于ML方法;对于标准误估计值,ML方法存在一定程度的低估,得到的置信区间覆盖比率也明显低于Diggle-Kenward选择模型。 相似文献
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Since the 16-17 centuries scientific methods have been playing a key role in the development of the scientific-technological age.There are three types of general scientific method,i.e.,induction-confirmation method,deduction-falsification method,and abduction-explanation method.They correspond to logical positivism,falsificationism,and historicism in philosophy of science respectively.Based on the reasoning procedure of these three scientific methods,and the development of the three schools of thought,this ... 相似文献
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