通用线性模型 Generalised Linear Models STATS4043_1

这是一份GLA格拉斯哥大STATS4043_1作业代写的成功案例

通用线性模型 Generalised Linear Models STATS4043_1

warning message, Clearly missing value cannot be allowed in certain contexts and wil1 be faulted, for instance an array cannot be given a shape containing a wissing value. In order to allow the user to detect missing values and replace them, if required, three special functions are supplied;
$$
\begin{aligned}
\text { \&EQMN }(X) &=1 \text { (true) if } x=* \
&=0 \text { (false) otherwise } \
\text { xMYV }(X ; Y) &=* \text { if } y=1 \quad \text { (true) } \
&=x \text { if } y=0 \quad \text { (false) } \
\not{\gamma V M}(X ; Y) &=x \text { if } x \neq * \
&=y \text { if } x=*
\end{aligned}
$$

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STATS4043_1 COURSE NOTES :

The likelihood function can be taken to be
$$
L(u)=\exp \left(-\frac{1}{2} \frac{\Sigma\left(y_{i}-\mu\right)^{2}}{a^{2}}\right)
$$
with $10 g-1$ ikelihood function
$$
\ell(\mu)=-\frac{1}{2} \frac{\varepsilon\left(\gamma_{i}-\mu\right)^{2}}{\sigma^{2}}
$$
Following through the usual maximam 1 ikelihood calculations, we have
$$
\begin{aligned}
\mathbb{E}^{\prime}(u) &=E\left(y_{i}-\mu\right) / d^{2} \
Q^{*}(\mu) &=n / o^{2}
\end{aligned}
$$









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