弹性回归 Flexible Regression STATS5052_1/STATS4040_1

这是一份GLA格拉斯哥大学MATHS3021_1作业代写的成功案例

弹性回归 Flexible Regression STATS5052_1/STATS4040_1

where $G_{0}$ is a standard exponential distribution. The distribution of $\epsilon_{t}$ is therefore
$$
f_{v, \xi}\left(\epsilon_{t}\right)=\sum_{j=1}^{\infty} w_{j} v\left(-\xi_{j} e^{-\lambda}, \xi_{j} e^{\lambda}\right)
$$
and the conditional return distribution is
$$
f_{G, \lambda}\left(\gamma_{t} \mid \sigma_{t}\right)=\sum_{j=1}^{\infty} w_{j} v\left(\gamma_{t} \mid-\xi_{j} \sigma_{t} e^{-\lambda}, \xi_{j} \sigma_{t} e^{\lambda}\right)
$$

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STATS5052_1/STATS4040_1 COURSE NOTES :

Utilising the latent variable, the copula yields
$$
P\left(Y_{1}=0, Y_{2} \leq Y_{2}\right)=P\left(Y_{1}^{} \leq 0, Y_{2} \leq Y_{2}\right)=C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right)
$$
and
$$
P\left(Y_{1}=1, Y_{2} \leq \gamma_{2}\right)=P\left(Y_{1}^{}>0, Y_{2} \leq \gamma_{2}\right)=F_{2}\left(\gamma_{2}\right)-C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right) \text {, }
$$
leading to the mixed binary-continuous density
$$
p\left(\gamma_{1}, \gamma_{2}\right)=\left(\frac{\partial C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right)}{\partial F_{2}\left(\gamma_{2}\right)}\right)^{1-\gamma_{1}} \cdot\left(1-\frac{\partial C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right)}{\partial F_{2}\left(\gamma_{2}\right)}\right)^{\gamma_{1}} \cdot p_{2}\left(\gamma_{2}\right),
$$









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