空间自回归-多元自适应广义泊松回归样条的混合模型

IF 1.4 Q3 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Septia Devi Prihastuti Yasmirullah, Bambang Widjanarko Otok, Jerry Dwi Trijoyo Purnomo, Dedy Dwi Prastyo
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引用次数: 1

摘要

几种多元自适应样条回归(MARS)方法可用于对分类和数值(特别是连续)数据进行建模。目前,还有其他数值数据类型——离散数据或计数数据——在建模时需要特别考虑。此外,经常观察到空间相关的计数数据。这一点在保健数据中得到了体现,例如新生儿死亡人数、结核病患者人数、医院访客人数等。然而,目前还没有结构一致的非参数回归和MARS模型来处理包含空间滞后自相关的计数数据。SAR-MAGPRS估计器(空间自回归-多元自适应广义泊松回归样条)的发展填补了这一空白。虽然它可以应用于不同的计数分布,但本研究是在广义泊松分布的假设下发展的。本文提供了一个信息理论框架,用于整合空间结构和非参数回归模型的知识,特别是用于计数数据类型的MARS。此外,拟议的方法可以在制定卫生政策时协助对疾病数量进行建模。该框架提出了惩罚最小二乘(PLS)方法在SAR - MAGPRS模型估计中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid model of spatial autoregressive-multivariate adaptive generalized Poisson regression spline
Several Multivariate Adaptive Regression Spline (MARS) approaches are available to model categorical and numerical (especially continuous) data. Currently, there are other numerical data types—discrete or count data—that call for specific consideration in modeling. Additionally, spatially correlated count data is frequently observed. This has been seen in the case of health data, for example, the number of newborn fatalities, tuberculosis patients, hospital visitors, etc. However, currently no structurally consistent nonparametric regression and MARS model for count data incorporating spatial lag autocorrelation. The SAR-MAGPRS estimator (Spatial Autoregressive - Multivariate Adaptive Generalized Poisson Regression Spline) is developed to fill this gap. Although it can be applied to different count distributions, the estimator was developed in this study under the assumption of a Generalized Poisson distribution. This paper provides an information-theoretic framework for incorporating knowledge of the spatial structure and non-parametric regression models, especially MARS for the count data types. Moreover, the proposed method can assist in modeling the number of diseases while health policies are being developed. The framework presents an application of the Penalized Least Square (PLS) method to estimate the SAR – MAGPRS model.
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来源期刊
Decision Science Letters
Decision Science Letters Decision Sciences-Decision Sciences (all)
CiteScore
3.40
自引率
5.30%
发文量
49
审稿时长
20 weeks
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