# (广义）线性模型|STAT3030/STATS5019 /STAT 504/STA600/Stat 539/STAT*6802/ST411/STAT 7430/SS 3860B(Generalized) Linear Models代写

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$$\mu_{t}=\sum_{i} \beta_{i} x_{i t}$$
However, this yields a conditional model of the form
$$\mu_{t \mid t-1}=\rho\left(y_{t-1}-\sum_{i} \beta_{i} x_{i, t-1}\right)+\sum_{i} \beta_{i} x_{i t}$$
This may also be written
$$\mu_{t \mid t-1}-\sum_{i} \beta_{i} x_{i t}=\rho\left(y_{t-1}-\sum_{i} \beta_{i} x_{i, t-1}\right)$$

## STA 144/STAT 451/STAT 506/STA 317 COURSE NOTES ：

where, again, $K$ is the unknown asymptotic maximum value. And again, we can obtain a linear structure, this time for a complementary log log link:
$$\log \left[-\log \left(\frac{K-y}{K}\right)\right]=\log (\alpha)+\beta t$$
We can use the same iterative procedure as before.