零膨胀

零膨胀模型（英語：Zero-inflated models）是人们在社会科学、自然中的计数资料的实际研究中，观察事件发生数中含有大量的零值。例如保险索赔次数，索赔数为0的概率很高，否则保险公司就面临破产风险。这种数据数资料中的零值过多，超出了Poisson分布等一般离散分布的预测能力。零膨胀这个概念首先是由 Lambert在1992年的论文“Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing”中提出^[1].。

1994年, Greene根据Lambert的方法提出了零膨胀负二项模型 (ZINB)^[2]。 2000年, Daniel根据Lambert的方法提出了零膨胀二项模型 (ZIB)^[3]。

注释参考[编辑]

^ Lambert, Diane. Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 1992, 34 (1): 1–14 [2012-12-14]. （原始内容存档于2021-03-08）.
^ Greene, William H. Some Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. Working Paper EC-94-10: Department of Economics, New York University. 1994 [2015-02-23]. （原始内容存档于2020-11-01）.
^ B. Hall, Daniel. Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study. Biometrics. 2000, 56 (4): 1030–1039 [2012-12-14]. （原始内容存档于2017-11-01）.

[1] Lambert, Diane. Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 1992, 34 (1): 1–14 [2012-12-14]. （原始内容存档于2021-03-08）.

[2] Greene, William H. Some Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. Working Paper EC-94-10: Department of Economics, New York University. 1994 [2015-02-23]. （原始内容存档于2020-11-01）.

[3] B. Hall, Daniel. Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study. Biometrics. 2000, 56 (4): 1030–1039 [2012-12-14]. （原始内容存档于2017-11-01）.

[1]

[2]

[3]