这是一份northeastern东北大学(美国) MATH 3081作业代写的成功案
$$
A\left(B_{Q}\right)=\prod_{h=1}^{r} A\left(B_{q h}\right)
$$
Using these points and weights, the response model becomes
$$
z_{i j q}=x_{i j}^{\prime} \beta+z_{i j}^{\prime} T B_{q},
$$
and so the conditional likelihood is
$$
\ell\left(\boldsymbol{Y}{i} \mid \boldsymbol{B}{q}\right)=\prod_{j=1}^{n_{i}} \Psi\left(z_{i j q}\right)^{Y_{i j}}\left[1-\Psi\left(z_{i j q}\right)\right]^{1-Y_{i j}}
$$
MATH 3081COURSE NOTES ：
$$
\frac{\partial \log L}{\partial \eta}=\sum_{i=1}^{N}\left[h\left(\boldsymbol{Y}{i}\right)\right]^{-1} \int{\boldsymbol{\theta}} \frac{\partial \ell_{i}}{\partial \eta} g(\boldsymbol{\theta}) d \boldsymbol{\theta}
$$
where
$$
\ell_{i}=\ell\left(\boldsymbol{Y}{i} \mid \boldsymbol{\theta}\right)=\prod{j=1}^{n_{i}} \prod_{c=1}^{C}\left(p_{i j c}\right)^{y_{i j c}}
$$
and
$$
p_{i j c}=P_{i j c}-P_{i j, c-1} .
$$