论文标题
广义添加剂模型以捕获加拿大Covid-19的死亡率
Generalized additive models to capture the death rates in Canada COVID-19
论文作者
论文摘要
为了捕获加拿大Covid-19的死亡率和强劲的每周，每两周和可能的每月模式，我们在没有直接基于统计学的感染率测量的情况下使用了广义的加性模型。通过检查一般和魁北克省的加拿大的死亡率，尤其是安大略省和艾伯塔省，人们可以轻松地发现，相对于泊松，有很大的过度分散，因此负二项式分布是分析的适当选择。广义添加剂模型（GAM）是用于数据分析的主要建模工具之一。游戏可以在回归模型的线性预测因子中有效地结合不同类型的固定，随机和平滑项，以说明不同类型的效果。 GAM是通用线性模型（GLM）的半参数扩展，当时没有先验原因选择特定响应函数，例如线性，二次，二次等，并且需要数据来“为自己说话”。 GAM通过平滑函数来执行此操作，并将每个预测变量在模型中分离为按“结”界定的部分，然后分别拟合多项式函数，并在结中没有链接 - 单独函数的第二个衍生物在结中是相等的。
To capture the death rates and strong weekly, biweekly and probably monthly patterns in the Canada COVID-19, we utilize the generalized additive models in the absence of direct statistically based measurement of infection rates. By examining the death rates of Canada in general and Quebec, Ontario and Alberta in particular, one can easily figured out that there are substantial overdispersion relative to the Poisson so that the negative binomial distribution is an appropriate choice for the analysis. Generalized additive models (GAMs) are one of the main modeling tools for data analysis. GAMs can efficiently combine different types of fixed, random and smooth terms in the linear predictor of a regression model to account for different types of effects. GAMs are a semi-parametric extension of the generalized linear models (GLMs), used often for the case when there is no a priori reason for choosing a particular response function such as linear, quadratic, etc. and need the data to 'speak for themselves'. GAMs do this via the smoothing functions and take each predictor variable in the model and separate it into sections delimited by 'knots', and then fit polynomial functions to each section separately, with the constraint that there are no links at the knots - second derivatives of the separate functions are equal at the knots.
