glmSTARMA - 一个用于拟合遵循广义线性模型的自回归时空模型的R包
glmSTARMA -- An R-Package for fitting autoregressive spatio-temporal models following generalized linear models
浏览论文内容
中文总结 AI 辅助
该研究介绍R包glmSTARMA，它基于广义线性模型方法，统一分析空间计数时间序列，实现自回归时空模型，还能对双广义线性模型推断，提供模型相关函数并举例说明，为时空数据分析提供新工具。
中文摘要 AI 辅助
R包glmSTARMA为固定位置的时空数据实现自回归模型，具有时不变空间依赖结构。它依赖广义线性模型方法，统一了几种分析空间计数时间序列的方法。此类模型允许响应的（条件）均值依赖于过去观测值、滞后（条件）期望和协变量，响应可以是连续或离散随机变量。此外，该包还为双广义线性模型进行推断，允许对边际分布的离散参数进行类似均值过程的建模。提供了模型估计、模拟、推断和预测的函数，并通过数据示例进行说明。
英文摘要
The R package glmSTARMA implements autoregressive models for spatio-temporal data at fixed locations, with time-invariant spatial dependency structure. We rely on generalized linear models methodology and unify several approaches for the analysis of spatial count time series. Such models allow the (conditional) mean of the response to depend on past observations, lagged (conditional) expectations, and covariates. The response can be a continuous or a discrete random variable. Additionally, the package develops inference for double generalized linear models, allowing the dispersion parameter(s) of the marginal distributions to be modeled similarly to the mean process. This is a new capability which introduces, for example, spatio-temporal volatility models, such as space-time GARCH processes, and count time series models with spatio-temporal overdispersion and underdispersion. We provide functions for model estimation, simulation, inference, and prediction. Its use is illustrated by data examples.
发表机构
- TU Dortmund University（多特蒙德工业大学）
- University of Cyprus（塞浦路斯大学）
机构由 AI 辅助整理，请以论文原文为准。
