高等数理统计学|MAST90123/STAT 550/Math 776Advanced Mathematical Statistics代写

0

这是一份非参数统计作业代写的成功案

非参数统计|STAT 8560/STAT 261/MATH335/PSY 610.01W/STAT 368/STAT 425/STAT 7610/MATH 494Nonparametric Statistics代写



For each $i$ compute $f\left(\Psi\left(t_{i}\right)\right) \delta t_{i}$. A little algebraic trickery yields
$$
\begin{aligned}
f\left(\Psi\left(t_{i}\right)\right) \delta t_{i} &=f\left(\Psi\left(t_{i}\right)\right) \delta t_{i} \frac{\Delta t_{i}}{\Delta t_{i}} \
&=f\left(\Psi\left(t_{i}\right)\right) \frac{\delta t_{i}}{\Delta t_{i}} \Delta t_{i}
\end{aligned}
$$
Sum over all $i$.
The integral $\int_{C} f(x, y) d \mathbf{s}$ is defined to be the limit of the sum from the previous step as $n \rightarrow \infty$. But as $n \rightarrow \infty$ it also follows that $\Delta t_{i} \rightarrow 0$. Our integrand contains the term $\frac{\delta_{i}}{\Delta t_{i}}$. As $\Delta t_{i} \rightarrow \infty$ this converges to $\left|\frac{\partial \Psi}{\partial t}\right|$. Hence, our integral has become
$$
\int_{C} f(x, y) d \mathbf{s}=\int_{a}^{b} f(\Psi(t))\left|\frac{\partial \Psi}{\partial t}\right| d t
$$



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MAST90123/STAT 550/Math 776 COURSE NOTES :


We now compute the area of each parallelogram.
Observe that each parallelogram is spanned by the vectors
$$
V_{u}=\Psi\left(u_{i+1}, v_{j}\right)-\Psi\left(u_{i}, v_{j}\right)
$$
and
$$
V_{v}=\Psi\left(u_{i}, v_{j+1}\right)-\Psi\left(u_{i}, v_{j}\right)
$$
The desired area is thus the magnitude of the cross product of these vectors:
$$
\text { Area }=\left|V_{u} \times V_{v}\right|
$$
We now do some algebraic tricks:
$$
\begin{aligned}
\left|V_{u} \times V_{v}\right| &=\left|V_{u} \times V_{v}\right| \frac{\Delta u \Delta v}{\Delta u \Delta v} \
&=\left|\frac{V_{u}}{\Delta u} \times \frac{V_{v}}{\Delta v}\right| \Delta u \Delta v
\end{aligned}
$$




高等数理统计学 | Advanced Mathematical Statistics代写 STAT3056代考

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这是一份anu澳大利亚国立大学 STAT3056作业代写的成功案

高等概率论 | Advanced Probability Theory 代写 STAT 7060代考
问题 1.

$$
\pi_{i j}=\pi_{i} \pi_{j}
$$
or
$$
\log \pi_{i j}=\log \pi_{i}+\log \pi_{j}
$$


证明 .

$$
\log \pi_{i j}=\alpha_{i}+\beta_{j}+\gamma_{i j}
$$
This form mimics the additive analysis of variance models introduced in chapter 12. The idea can readily be extended to higher-order tables. For example, a possible model for a three-way table is
$$
\log \pi_{i j k}=\alpha_{i}+\beta_{j}+\delta_{i j}+\epsilon_{i k}+\gamma_{j k}
$$

.

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STAT3056 COURSE NOTES :


$$
v_{i}=\frac{y_{i}-\bar{y}}{\sqrt{s_{y y}}}
$$
We then have $s_{u u}=s_{v v}=1$ and $s_{u v}=r$. The least squares line for predicting $v$ from $u$ thus has slope $r$ and intercept
$$
\bar{\beta}{0}=\bar{v}-r \bar{u}=0 $$ and the predicted values are $$ \hat{v}{i}=r u_{i}
$$