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Order shrinkage and selection for the INGARCH(p,q) model
International Journal of Biomathematics ( IF 2.2 ) Pub Date : 2021-06-11 , DOI: 10.1142/s1793524521500704
Yuan Tian 1 , Dehui Wang 2 , Xinyang Wang 3
Affiliation  

The integer-valued generalized autoregressive conditional heteroskedastic (INGARCH) model is often utilized to describe data in biostatistics, such as the number of people infected with dengue fever, daily epileptic seizure counts of an epileptic patient and the number of cases of campylobacterosis infections, etc. Since the structure of such data is generally high-order and sparse, studies about order shrinkage and selection for the model attract many attentions. In this paper, we propose a penalized conditional maximum likelihood (PCML) method to solve this problem. The PCML method can effectively select significant orders and estimate the parameters, simultaneously. Some simulations and a real data analysis are carried out to illustrate the usefulness of our method.

中文翻译:

INGARCH(p,q) 模型的阶次收缩和选择

整数值广义自回归条件异方差(INGARCH)模型常用于描述生物统计学中的数据,如登革热感染人数、癫痫患者每日癫痫发作次数和弯曲杆菌感染病例数等。 . 由于此类数据的结构一般是高阶和稀疏的,因此关于模型的阶收缩和选择的研究备受关注。在本文中,我们提出了一种惩罚条件最大似然(PCML)方法来解决这个问题。PCML 方法可以同时有效地选择重要阶数和估计参数。进行了一些模拟和实际数据分析,以说明我们方法的有用性。
更新日期:2021-06-11
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