Linear regression with randomly double-truncated data
Linear regression with randomly double-truncated data
复制标题
具有随机双截断数据的线性回归
DOI：
10.37920/sasj.2017.51.1.1
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发表时间：
2017
影响因子：
0.3
通讯作者：
A. Dörre
中科院分区：
文献类型：
--
作者：
G. Frank;A. Dörre
研究了随机双截尾下线性回归模型的非参数估计，即变量可观测当且仅当因变量落在随机区间内。该方法只需要弱分布假设，以确保可识别性，但不需要任何特定的分布族的任何变量，无论是截断变量，也不为误差项。利用几种分布函数的非参数估计，建立了相合和渐近正态估计。模拟研究表明，即使对于相同数量的观测值，观测值的概率越低，估计量的均方误差越高。最后，该方法被应用到一个双重截断的数据集的德国公司，年龄在破产的利益。关键词：破产风险，线性回归，非参数，随机双截尾
Non-parametric estimation for a linear regression model under random double-truncation is investigated, i.e. the variables are observed if and only if the dependent variable lies in a random interval. The method requires only weak distribution assumptions to ensure identifiability, but does not require any specific distribution family for any variable, neither for the truncation variables nor for the error term. By using non-parametric estimators of several distribution functions, consistent and asymptotically normal estimators are established. A simulation study shows the tendency that the lower the probability of observation, the higher the mean squared error of the estimators, even for the same number of observations. Finally, the method is applied to a doubly truncated data set of German companies, where the age-at-insolvency is of interest. Keywords: Insolvency risk, Linear regression, Non-parametric, Random double-truncation