随机过程与金融|MA50089 Stochastic processes & finance代写

0

To perform simple calculations to compute certain quantities relating to Brownian motion, and to understand how these quantities can be important in pricing financial derivatives

这是一份Bath巴斯大学MA50089作业代写的成功案

随机过程与金融|MA50089 Stochastic processes & finance代写

In the univariate two-way model, we measure one dependent variable $y$ on each experimental unit. The balanced two-way fixed-effects model with factors $A$ and $B$ is
$$
\begin{aligned}
y_{i j k} &=\mu+\alpha_{i}+\beta_{j}+\gamma_{i j}+\varepsilon_{i j k} \
&=\mu_{i j}+\varepsilon_{i j k}, \
i &=1,2, \ldots, a, \quad j=1,2, \ldots, b, \quad k=1,2, \ldots, n
\end{aligned}
$$
where $\alpha_{i}$ is the effect (on $y_{i j k}$ ) of the $i$ th level of $A, \beta_{j}$ is the effect of the $j$ th level of $B, \gamma_{i j}$ is the corresponding interaction effect, and $\mu_{i j}$ is the population mean for the $i$ th level of $A$ and the $j$ th level of $B$. In order to obtain $F$-tests, we further assume that the $\varepsilon_{i j k}$ ‘s are independently distributed as $N\left(0, \sigma^{2}\right)$.

Let $\bar{\mu}{i .}=\sum{j} \mu_{i j} / b$ be the mean at the $i$ th level of $A$ and define $\bar{\mu}{. j}$ and $\bar{\mu}{. .}$ similarly. Then if we use side conditions $\sum_{i} \alpha_{i}=\sum_{j} \beta_{j}=\sum_{i} \gamma_{i j}=\sum_{j} \gamma_{j}=0$,


英国论文代写Viking Essay为您提供实分析作业代写Real anlysis代考服务

MA50089 COURSE NOTES :

0 can be tested by $T^{2}$ or any of the four MANOVA test statistics. To test $H_{0}: \sum_{i} c_{i} \overline{\boldsymbol{\mu}}{i}=\mathbf{0}$, for example, we can use $$ T^{2}=\frac{n b}{\sum{i=1}^{a} c_{i}^{2}}\left(\sum_{i=1}^{a} c_{i} \overline{\mathbf{y}}{i . .}\right)^{\prime}\left(\frac{\mathbf{E}}{v{E}}\right)^{-1}\left(\sum_{i=1}^{a} c_{i} \overline{\mathbf{y}}{i . .}\right) $$ which is distributed as $T{p, v_{E}}^{2}$ when $H_{0}$ is true. Alternatively, the hypothesis matrix
$$
\mathbf{H}{1}=\frac{n b}{\sum{i=1}^{a} c_{i}^{2}}\left(\sum_{i=1}^{a} c_{i} \overline{\mathbf{y}}{i . .}\right)\left(\sum{i=1}^{a} c_{i} \overline{\mathbf{y}}{i . .}\right)^{\prime} $$ can be used in $$ \Lambda=\frac{|\mathbf{E}|}{\left|\mathbf{E}+\mathbf{H}{1}\right|}
$$