如果你也在 怎样代写广义线性模型Generalized linear model 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。广义线性模型Generalized linear model通过允许响应变量具有任意分布(而不是简单的正态分布)，以及响应变量的任意函数(链接函数)随预测器线性变化(而不是假设响应本身必须线性变化)，涵盖了所有这些情况。例如，上面预测海滩出席人数的情况通常会用泊松分布和日志链接来建模，而预测海滩出席率的情况通常会用伯努利分布(或二项分布，取决于问题的确切表达方式)和对数-几率(或logit)链接函数来建模。

广义线性模型Generalized linear model普通线性回归将给定未知量(响应变量，随机变量)的期望值预测为一组观测值(预测因子)的线性组合。这意味着预测器的恒定变化会导致响应变量的恒定变化(即线性响应模型)。当响应变量可以在任意一个方向上以良好的近似值无限变化时，或者更一般地适用于与预测变量(例如人类身高)的变化相比仅变化相对较小的任何数量时，这是适当的。

couryes-lab™ 为您的留学生涯保驾护航 在代写广义线性模型generalized linear model方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写广义线性模型generalized linear model代写方面经验极为丰富，各种代写广义线性模型generalized linear model相关的作业也就用不着说。

统计代写|广义线性模型代写generalized linear model代考|The power family

The power function may be extended so that it is regarded as a family of distributions. That is, we may model data as a power model, like a gamma or logit model. The difference is that the power distribution is not directly based on a probability distribution, that is, a member of the exponential family of distributions.
Although the log-likelihood and deviance functions were derived directly from the exponential family form of a probability distribution, one can derive a working quasideviance from the variance function. See chapter 17 on quasilikelihood for more details. The quasideviance for the power family is
$$
\frac{2 y}{(1-a)\left(y^{1-a}-\mu^{1-a}\right)}-\frac{2}{(2-a)\left(y^{2-a}-\mu^{2-a}\right)}
$$
One can then create a power algorithm that uses the above deviance function with the power link, the power inverse link, the derivative of the power link, and the power variance functions. This is an example of a quasideviance model; one that is an extended member of the GLM family.

One use of power links is to assess optimal models within a GLM distribution family. One can use the link function to check various statistics resulting from changes to $a$ in the power link. This gives a wide variety of possible links for each continuous family.

However, we may need to use a general power link model to assess models between families. Of course, we may simply create a series of models using a continuum of links within each of the Gaussian $(a=1)$, Poisson $(a=0)$, gamma ( $a=-1$ ), and inverse Gaussian $(a=-2$ ) families. If all other aspects of the models are the same, then the model having the lower deviance value and bestfitting residuals will be the model of choice. However, we are thereby giving wide leniency to the notion of “aspects”.

统计代写|广义线性模型代写generalized linear model代考|The binomial–logit family

Except for the canonical Gaussian model, binomial models are used more often than any other member of the GLM family.
Binomial regression models are used in analyses having discrete (corresponding to the number of successes for $k$ trials ) or grouped responses. As shown in table 9.1 , models of binary responses are based on the Bernoulli $(n=1$ ) or binomial distribution. The Bernoulli distribution is a degenerate case of the binomial, where the number of trials is equal to 1 .
Table 9.1: Binomial regression models
\begin{tabular}{ll}
Response & Model \
\hline Binary: ${0,1}$ & $\operatorname{Bernoulli}=\operatorname{Binomial}(1)$ \
Proportional: ${0,1, \ldots, k}$ & $\operatorname{Binomial}(k)$
\end{tabular}
Proportional-response data involve two variables corresponding to the number of successes, $y_i$, of a population of $k_i$ trials , where both variables are indexed by $i$ because grouped data do not require that the number of trials be the same for each observation. Hence, a binomial model can be considered as a weighted binary response model or simply as a two-part response relating the proportion of successes to trials. We discuss the relationship between these two methods of dealing with discrete data. Binary data can be modeled as grouped, but the reverse is not always possible.

Binary responses take the form of 0 or 1 , with 1 typically representing a success or some other positive result, whereas 0 is taken as a failure or some negative result. Responses may also take the form of $1 / 2, a / b$, or a similar pair of values. However, values other than 0 and 1 are translated to a $0 / 1$ format by the binary regression algorithm before estimation. The reason for this will be apparent when we describe the probability function. Also in Stata, a 0 indicates failure and any other nonzero nonmissing value represents a success in Bernoulli models.

广义线性模型代考

统计代写|广义线性模型代写generalized linear model代考|The power family

幂函数可以被扩展成一个分布族。也就是说，我们可以将数据建模为幂模型，就像gamma或logit模型一样。不同之处在于，功率分布不是直接基于概率分布，即指数分布族的成员。
虽然对数似然函数和偏差函数是直接从概率分布的指数族形式推导出来的，但人们可以从方差函数中推导出工作准证据。有关更多细节，请参见第17章准可能性。幂族的拟方差为
$$
\frac{2 y}{(1-a)\left(y^{1-a}-\mu^{1-a}\right)}-\frac{2}{(2-a)\left(y^{2-a}-\mu^{2-a}\right)}
$$
然后可以创建一个功率算法，该算法将上述偏差函数与功率链路、功率逆链路、功率链路的导数和功率方差函数结合使用。这是准证据模型的一个例子;它是GLM家族的延伸成员。

功率链路的一个用途是评估GLM分布族中的最优模型。可以使用link函数检查电源链接中$a$更改所产生的各种统计数据。这为每个连续的家族提供了多种可能的联系。

然而，我们可能需要使用一个通用的功率连接模型来评估家庭之间的模型。当然，我们可以简单地使用在每个高斯$(a=1)$、泊松$(a=0)$、伽马($a=-1$)和逆高斯$(a=-2$族中的连续连接来创建一系列模型。如果模型的其他方面都相同，则选择偏差值较小且残差最适合的模型。然而，我们因此对“方面”的概念给予了宽大处理。

统计代写|广义线性模型代写generalized linear model代考|The binomial–logit family

除了典型高斯模型外，二项模型比GLM家族的任何其他成员都更常用。
二项回归模型用于具有离散(对应于$k$试验的成功次数)或分组响应的分析。如表9.1所示，二元响应模型基于伯努利($(n=1$)或二项分布。伯努利分布是二项分布的退化情况，其中试验次数等于1。
表9.1:二项回归模型
\begin{tabular}{ll}
Response & Model \hline Binary: ${0,1}$ & $\operatorname{Bernoulli}=\operatorname{Binomial}(1)$ \Proportional:${0,1, \ldots, k}$ & $\operatorname{Binomial}(k)$
\end{tabular}
比例反应数据涉及两个变量，对应于$k_i$试验总体的成功次数$y_i$，其中两个变量都以$i$为索引，因为分组数据不要求每次观察的试验次数相同。因此，二项模型可以被视为加权二元响应模型，或者简单地视为与成功与试验的比例相关的两部分响应。我们讨论了这两种处理离散数据的方法之间的关系。二进制数据可以按分组建模，但反过来并不总是可能的。

二进制响应采用0或1的形式，其中1通常代表成功或其他积极结果，而0则被视为失败或某些消极结果。响应也可以采用$1 / 2, a / b$或类似的值对的形式。但是，在估计之前，除0和1之外的值由二元回归算法转换为$0 / 1$格式。当我们描述概率函数时，这样做的原因将是显而易见的。同样在Stata中，0表示失败，任何其他非零的非缺失值在伯努利模型中表示成功。

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