论文标题
偏斜的逻辑分布，并应用于建模COVID-19
A skew logistic distribution with application to modelling COVID-19 epidemic waves
论文作者
论文摘要
通过引入偏斜参数，提出了对称逻辑分布的新颖而简单的扩展。显示如何使用最大似然估计随后的偏差逻辑分布的三个参数。然后将偏斜的逻辑分布扩展到偏斜的双逻辑分布，以允许在流行时间时间序列数据中对多波的建模。提出的偏斜模型在英国的COVID-19数据上进行了验证，并通过使用最近配制的经验生存的Jensen-Shannon Divergence（$ {\ cal e} SJS $）和Kolmmogorov-Smirnov Tripample tat-sample Test statistic（$ ks2 $ ks2 $ ks2 $）进行了评估，并通过最近配制的经验生存Jensen-Shannon Divergence（$ {\ cal e} SJS $）评估了对逻辑和正常分布的优点。我们采用95 \％的自举置信区间来评估偏差分布与其他分布相对于其他分布的融合优点的改善。 $ {\ cal e} sjs $获得的置信区间比使用此数据集的$ ks2 $要窄，这意味着$ {\ cal e} sjs $比$ ks2 $更强大。
A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen-Shannon divergence (${\cal E}SJS$) and the Kolmogorov-Smirnov two-sample test statistic ($KS2$). We employ 95\% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ${\cal E}SJS$ are narrower than those for the $KS2$ on using this data set, implying that the ${\cal E}SJS$ is more powerful than the $KS2$.
