线性混合模型 Linear Mixed Models STATS5054_1/STATS4045_1

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线性混合模型 Linear Mixed Models STATS5054_1/STATS4045_1

The methods discussed above for the full parameter vector now directly carry over to calculate local influences on the geometric surface defined by $\mathrm{LD}{1}(\omega)$. We now partition $\ddot{L}$ as $$ \ddot{L}=\left(\begin{array}{ll} \ddot{L}{11} & \ddot{L}{12} \ \ddot{L}{21} & \ddot{L}{22} \end{array}\right), $$ according to the dimensions of $\theta{1}$ and $\theta_{2}$. Cook (1986) has then shown that the local influence on the estimation of $\theta_{1}$, of perturbing the model in the direction of a normalized vector $\boldsymbol{h}$, is given by
$$
C_{h}\left(\boldsymbol{\theta}{1}\right)=2\left|\boldsymbol{h}^{\prime} \Delta^{\prime}\left[\ddot{L}^{-1}-\left(\begin{array}{cc} 0 & 0 \ 0 & \ddot{L}{22}^{-1}
\end{array}\right)\right] \Delta \boldsymbol{h}\right|
$$
Because all eigenvalues of the malrix
$$
\left(\begin{array}{ll}
\ddot{L}{11} & \ddot{L}{12} \
\ddot{L}{21} & \ddot{L}{22}
\end{array}\right)\left(\begin{array}{cc}
0 & 0 \
0 & \ddot{L}{22}^{-1} \end{array}\right)=\left(\begin{array}{cc} 0 & \ddot{L}{12} \ddot{L}_{22}^{-1} \
0 & I
\end{array}\right)
$$

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MATHS5077_1 COURSE NOTES :

A measure of influence, proposed , is then
$$
\rho_{i}=-\left(\hat{\boldsymbol{\theta}}-\hat{\boldsymbol{\theta}}{(i)}^{1}\right)^{\prime} \ddot{L}\left(\hat{\boldsymbol{\theta}}-\hat{\boldsymbol{\theta}}{(i)}^{1}\right)
$$$$
\boldsymbol{\Delta}{i}=-\sum{j \neq i} \boldsymbol{\Delta}{j}=\ddot{L}{(i)}(\widehat{\boldsymbol{\theta}})\left(\hat{\boldsymbol{\theta}}{(i)}^{1}-\widehat{\boldsymbol{\theta}}\right) $$ such that expression (11.4) becomes $$ C{i}=-2\left(\hat{\boldsymbol{\theta}}-\hat{\boldsymbol{\theta}}{(i)}^{1}\right)^{\prime} \ddot{L}{(i)} \ddot{L}^{-1} \ddot{L}{(i)}\left(\hat{\boldsymbol{\theta}}-\hat{\boldsymbol{\theta}}{(i)}^{1}\right)
$$









代数专题 : Topics in Algebra MATHS5077_1

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这是一份GLA格拉斯哥大MATHS5077_1作业代写的成功案例

代数专题 : Topics in Algebra MATHS5077_1

Although the inverse of a homomorphism may not exist, a homomorphism does share some properties in common with isomorphisms. For example, if $\theta: G \rightarrow$ $G^{\prime}$ is a homomorphism, and if $e$ and $e^{\prime}$ are the identities in $G$ and $G^{\prime}$, respectively, then $\theta(e)=\theta\left(e^{2}\right)=\theta(e) \theta(e)$, so that $\theta(e)=e^{\prime}$. Thus a homomorphism maps the identity in $G$ to the identity in $G^{\prime}$. Similarly, as $e^{\prime}=\theta(e)=\theta\left(g g^{-1}\right)=$ $\theta(g) \theta\left(g^{-1}\right)$, we see that for all $g$ in $G$
$$
\theta(g)^{-1}=\theta\left(g^{-1}\right) .
$$
The most obvious example of a homomorphism is a linear map between vector spaces. Indeed, a vector space is a group with respect to addition, and as any linear map $\alpha: V \rightarrow W$ satisfies
$$
\alpha(u+v)=\alpha(u)+\alpha(v)
$$

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MATHS5077_1 COURSE NOTES :

Proof First, every element of $x_{0} K$ is a solution to the given equation, for the general element of $x_{0} K$ is $x_{0} k$, where $k \in K$, and $\theta\left(x_{0} k\right)=\theta\left(x_{0}\right) \theta(k)=y e^{\prime}=$ $y$. Next suppose that $\theta(x)=y$, and consider the element $x_{0}^{-1} x$ in $G$. Then
$$
\theta\left(x_{0}^{-1} x\right)=\theta\left(x_{0}^{-1}\right) \theta(x)=\left(\theta\left(x_{0}\right)\right)^{-1} y=y^{-1} y=e^{\prime},
$$
so that $x_{0}^{-1} x \in K$. This means that $x \in x_{0} K$ and so the set of solutions of $\theta(x)=y$ is the coset $x_{0} K$.









通用线性模型 Generalised Linear Models STATS4043_1

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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
$$
\begin{aligned}
\mathbb{E}^{\prime}(u) &=E\left(y_{i}-\mu\right) / d^{2} \
Q^{*}(\mu) &=n / o^{2}
\end{aligned}
$$









数学物理 Mathematical Physics MATHS5073_1 /MATHS4107_1

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数学物理 Mathematical Physics MATHS5073_1 /MATHS4107_1

Now approaching this problem using vector notation, we note that the torque about the fulcrum due to $F_{1}$ is $\tau_{1}=r_{1} \times F_{1}$. Here $r_{1}$ is a vector from the fulcrum to the point of application of $\mathbf{F}{1}$, and $$ \tau{1}=\mathbf{r}{1} \times \mathbf{F}{1}=r_{1} F_{1} \sin \theta_{1} \hat{\mathbf{n}}=d_{1} F_{1} \hat{\mathbf{n}}
$$
where $\hat{\mathbf{n}}$ is a unit vector normal to, and out of the plane of the figure. A similar analysis of the torque $\tau_{2}$ reveals that it is
$$
\tau_{2}=\mathbf{r}{2} \times \mathbf{F}{2}=-r_{2} F_{2} \sin \theta_{2} \hat{\mathbf{n}}=-d_{2} F_{2} \hat{\mathbf{n}}
$$
the minus sign occurring because $\hat{\mathbf{n}}$ has the same meaning as before but this torque is directed into the plane of the figure. We now observe that $\theta_{2}=\pi-\theta_{1}$, and therefore $\sin \theta_{2}=\sin \theta_{1}$. At equilibrium the sum of these torques is zero.

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MATHS5073_1 /MATHS4107_1 COURSE NOTES :

Sometimes the determination of the gradient is straightforward. Given
$$
f(x, y, z)=\frac{x y}{z}
$$
we easily find
$$
\frac{\partial f}{\partial x}=\frac{y}{z}, \quad \frac{\partial f}{\partial y}=\frac{x}{z}, \quad \frac{\partial f}{\partial z}=-\frac{x y}{z^{2}}
$$
leading to $\nabla f=\frac{y}{z} \hat{\mathbf{e}}{x}+\frac{x}{z} \hat{\mathbf{e}}{y}-\frac{x y}{z^{2}} \hat{\mathbf{e}}_{z}$









弹性回归 Flexible Regression STATS5052_1/STATS4040_1

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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
$$
p\left(\gamma_{1}, \gamma_{2}\right)=\left(\frac{\partial C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right)}{\partial F_{2}\left(\gamma_{2}\right)}\right)^{1-\gamma_{1}} \cdot\left(1-\frac{\partial C\left(F_{1}^{}(0), F_{2}\left(\gamma_{2}\right)\right)}{\partial F_{2}\left(\gamma_{2}\right)}\right)^{\gamma_{1}} \cdot p_{2}\left(\gamma_{2}\right),
$$









经典场论 Classical Field Theory MATHS5054_1

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这是一份GLA格拉斯哥大学MATHS3021_1作业代写的成功案例

经典场论 Classical Field Theory MATHS5054_1

We consider an applied field $E_{0}$ that is small compared to the internal fields of the atoms. that is,
$$
E_{0} \sim \frac{e}{a_{B}^{2}} \sim \frac{26 \mathrm{Volts}}{a_{B}} \sim 5 \times 10^{\circ} \mathrm{Volts} / \mathrm{cm}
$$
where $e$ is the electron’s charge and $a_{B}$ the Bohr radius:
$$
a_{N}=\frac{\hbar^{2}}{m e^{2}} \sim \frac{1}{2} \times 10^{-8} \mathrm{~cm}
$$
Here, $\hbar$ is Planck’s constant divided by $2 \pi$ and $m$ is the electron mass.

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MATHS5054_1 COURSE NOTES :

$$
Q_{u j}=\alpha_{Q}\left(\frac{\partial E_{1}}{\partial x_{j}}+\frac{\partial E_{i}}{\partial x_{j}}\right)
$$
The quadrupole polarizability $\alpha_{O}$ in has the dimensionality $L^{S}$. so we expect $\alpha_{Q}$ for an atom to be $\sim a_{m}^{5}$. The field generated by the induced moment, in analogy with, will be
$$
\delta E_{Q} \sim \alpha_{\circlearrowright} \frac{\partial E_{i 1}}{\partial x} \sum_{i} \frac{1}{\left|\mathbf{r}-\mathbf{r}{i}\right|^{+}} $$ or $$ \delta E{\circlearrowright}-\frac{E_{11}}{R} \times \frac{N \alpha_{Q}}{R^{4}}
$$









数学 MATHS 3R MATHS3021_1/MATHS2035_1

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数学 MATHS 3R MATHS3021_1/MATHS2035_1

which is bounded above by $4 H K$ where
$$
H=\limsup {k, \delta} \frac{\delta}{c(\delta)} \int{0}^{\infty} \psi_{\delta}(v) \sigma_{k \delta-v}^{2} \mathrm{~d} v
$$
and
$$
K=\frac{\delta}{c(\delta)} \sum_{k=1}^{n}\left(\int_{0}^{\infty} \chi_{\delta}(v) a_{k \delta-v} \mathrm{~d} v\right)^{2}
$$
Here
$$
\int_{0}^{\infty} \psi_{\delta}(v) \sigma_{k \delta-v}^{2} \mathrm{~d} v \leq C c(\delta)
$$
where the constant $C$ depends on $t$ and $\sigma$. Hence $H \rightarrow 0$. Furthermore,
$$
\begin{aligned}
\sum_{k=1}^{n}\left(\int_{0}^{\infty} \chi_{\delta}(v) a_{k \delta-v} \mathrm{~d} v\right)^{2} & \leq C \sum_{k=1}^{n}\left(\int_{0}^{\infty}\left|\chi_{\delta}(v)\right| \mathrm{d} v\right)^{2} \
&=C \delta^{-1}\left(\int_{0}^{\infty}\left|\chi_{\delta}(v)\right| \mathrm{d} v\right)^{2}
\end{aligned}
$$
where $C$, again, depends on $t$ and $a$. Hence Condition A implies $K \rightarrow 0$.

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MATHS3021_1/MATHS2035_1 COURSE NOTES :

Here we find
$$
c_{1}(\delta)=\frac{1}{2 \lambda}\left(1-e^{-2 \lambda \delta}\right) \sim \delta
$$
while for $k=2, \ldots, n$
$$
c_{k}(\delta)=\frac{1}{2 \lambda}\left(e^{\lambda \delta}-1\right)^{3} e^{-2 k \lambda} \sim \frac{\lambda^{2}}{2} e^{-2 k \lambda} \delta^{3} .
$$
Moreover we have
$$
c_{n+1}(\delta)=\frac{1}{2 \lambda}\left(1-e^{-2 \lambda \delta}\right) \sim e^{-2 \lambda l} \delta,
$$
whereas $c_{k}(\delta)=0$ for $k>n+1$. Finally, $c(\delta) \sim \delta\left(1+e^{-2 \lambda l}\right)$ and
$$
\bar{c}{n+1}(\delta) c(\delta)^{-1} \int{l}^{l+\delta} g^{2}(v-\delta) \mathrm{d} v \rightarrow\left(1+e^{2 \lambda l}\right)^{-1} .
$$
So, Conditions A, C and D are met. But Condition B is not and we have that $\pi_{\delta} \rightarrow \pi$, where
$$
\pi=\frac{1}{1+e^{-2 \lambda l}} \delta_{0}+\frac{1}{1+e^{2 \lambda l}} \delta_{1},
$$









统计遗传学 Statistical Genetics STATS4074_1/STATS5011_1

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统计遗传学 Statistical Genetics STATS4074_1/STATS5011_1

$$
\begin{aligned}
&d_{i, i}=0 \
&d_{i, j}=d_{j, i}>0 \text { for } i \neq j ; \text { and } \
&d_{i, j} \leq d_{i, k}+d_{j, k} \text { (this being the triangle inequality). }
\end{aligned}
$$
The first two are straightforward, each thing is identical to itself $\left(d_{i, i}=0\right)$; and the difference between $\mathrm{A}$ and $\mathrm{B}$ must be the same as between $\mathrm{B}$ and $\mathrm{A}$ (thus $d_{i, j}=d_{j, i}$ ); and the value must be positive when they differ. The third part of the definition, the triangle inequality is also a simple concept; the direct distance between London and Sydney cannot

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STATS4074_1/STATS5011_1 COURSE NOTES :

From regression theory, the vector of partial regression coefficients for predicting the value of $y$ given a vector of observations $\mathbf{z}$ is $\mathbf{P}^{-1} \sigma(\mathbf{z}, y)$, where $\mathbf{P}$ is the covariance matrix of $\mathbf{z}$, and $\sigma(\mathbf{z}, y)$ is the vector of covariances between the elements of $\mathbf{z}$ and the variable $y$. Since $\boldsymbol{S}=\sigma(\mathbf{z}, \omega)$, it immediately follows that
$$
\mathbf{P}^{-1} \sigma(\mathbf{z}, \omega)=\mathbf{P}^{-1} \mathbf{S}=\boldsymbol{\beta}
$$
is the vector of partial regression for the best linear regression of relative fitness $\omega$ on phenotypic value $\mathbf{z}$, viz.,
$$
\omega(\mathbf{z})=1+\sum_{j=1}^{n} \beta_{j}\left(z_{j}-\mu_{j}\right)=1+\beta^{\mathrm{T}}(\mathbf{z}-\boldsymbol{\mu}) .
$$









代数 Algebra MATHS4072_1

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代数 Algebra MATHS4072_1

$$
\rho\left(J_{p}\right)<\frac{p-1-s}{p-2}
$$
then the regions of convergence of the SOR method $\left(\rho\left(\mathscr{L}{\omega}\right)<1\right)$ are For $s=1, \quad \omega \in\left(0, \frac{p}{p-1}\right)$ and for $s=-1, \omega \in\left(\frac{p-2}{p-1}, \frac{2}{1+\rho\left(J{p}\right)}\right)$.

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MATHS4072_1 COURSE NOTES :

$$
\begin{aligned}
&\dot{x}^{k}(t)=G\left(t, x^{k}(t), x^{k-1}(t)\right), t \in[0, T], \
&x^{k}(0)=x_{0}
\end{aligned}
$$
for $k=1,2, \ldots .$ Here, the function $x^{k-1}$ is known and $x^{k}$ is to be determined.
Note that the familiar Picard iteration
$$
\begin{aligned}
&\dot{x}^{k}(t)=F\left(t, x^{k-1}(t)\right), t \in[0, T], \
&x^{k}(0)=x_{0}
\end{aligned}
$$









概率与统计学原理 Principles of Prob & Stats (M) STATS5022_1/STATS4047_1

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概率与统计学原理 Principles of Prob & Stats (M) STATS5022_1

Here the population mean and the standard deviation are $\mu=110$ and $\sigma=10$, respectively. The sample size $n=75$ is large, so the central limit theorem ensures that the distribution of $\bar{X}$ is approximately normal with
Mean of $\bar{X}=110$
Standard deviation of $\bar{X}=\frac{\sigma}{\sqrt{n}}=\frac{10}{\sqrt{75}}=1.155$
To find $P[109<\bar{X}<112]$ we convert to the standardized variable
$$
Z=\frac{\bar{X}-110}{1.155}
$$
and calculate the z-values
$$
\frac{109-110}{1.155}=-.866, \quad \frac{112-110}{1.155}=1.732
$$

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STATS5022_1/STATS4047_1 COURSE NOTES :

$$
\bar{x}=\$ 227 \quad \text { and } \quad s=\$ 15
$$
With $1-\alpha=.90$ we have $\alpha / 2=.05$, and $z_{\alpha / 2}=1.645$
$$
1.645 \frac{s}{\sqrt{n}}=\frac{1.645 \times 15}{\sqrt{75}}=2.85
$$
Hence, a $90 \%$ confidence interval for the population mean $\mu$ is