Sofikitou Elisavet M, Liu Ray, Wang Huipei, Markatou Marianthi
Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA.
Head of Oncology Data Science, AstraZeneca PLC, Gaithersburg, MD 20878, USA.
Entropy (Basel). 2021 Jan 14;23(1):107. doi: 10.3390/e23010107.
Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.
皮尔逊残差有助于识别模型误设，因为它们将使用数据估计的模型与原假设下假定的模型进行比较。我们提出了考虑数据测量尺度的皮尔逊残差系统的不同公式，并研究了它们的性质。我们进一步关注混合尺度数据的情况，即同时以分类尺度和区间尺度测量的数据。我们研究了混合尺度数据情况下获得的最小差异估计量的渐近性质和稳健性，并通过模拟举例说明了这些方法的性能。
