### 统计代写|广义线性模型代写generalized linear model代考|STAT3030

statistics-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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|广义线性模型代写generalized linear model代考|Properties of the Estimator

The least squares estimator $\hat{\boldsymbol{\beta}}$ of Equation $2.7$ has several interesting properties. If the model is correct, in the (weak) sense that the expected value of the response $Y_i$ given the predictors $\mathbf{x}_i$ is indeed $\mathbf{x}_i^{\prime} \boldsymbol{\beta}$, then the OLS estimator is unbiased, its expected value equals the true parameter value:
$$\mathrm{E}(\hat{\boldsymbol{\beta}})=\boldsymbol{\beta} .$$
It can also be shown that if the observations are uncorrelated and have constant variance $\sigma^2$, then the variance-covariance matrix of the OLS estimator is
$$\operatorname{var}(\hat{\boldsymbol{\beta}})=\left(\mathbf{X}^{\prime} \mathbf{X}\right)^{-1} \sigma^2 .$$
This result follows immediately from the fact that $\hat{\boldsymbol{\beta}}$ is a linear function of the data $\mathbf{y}$ (see Equation 2.7), and the assumption that the variance-covariance matrix of the data is $\operatorname{var}(\mathbf{Y})=\sigma^2 \mathbf{I}$, where $\mathbf{I}$ is the identity matrix.

A further property of the estimator is that it has minimum variance among all unbiased estimators that are linear functions of the data, i.e.

it is the best linear unbiased estimator (BLUE). Since no other unbiased estimator can have lower variance for a fixed sample size, we say that OLS estimators are fully efficient.

Finally, it can be shown that the sampling distribution of the OLS estimator $\hat{\boldsymbol{\beta}}$ in large samples is approximately multivariate normal with the mean and variance given above, i.e.
$$\hat{\boldsymbol{\beta}} \sim N_p\left(\boldsymbol{\beta},\left(\mathbf{X}^{\prime} \mathbf{X}\right)^{-1} \sigma^2\right)$$

## 统计代写|广义线性模型代写generalized linear model代考|Estimation of 2

Substituting the OLS estimator of $\boldsymbol{\beta}$ into the log-likelihood in Equation $2.5$ gives a profile likelihood for $\sigma^2$
$$\log L\left(\sigma^2\right)=-\frac{n}{2} \log \left(2 \pi \sigma^2\right)-\frac{1}{2} \operatorname{RSS}(\hat{\boldsymbol{\beta}}) / \sigma^2 .$$
Differentiating this expression with respect to $\sigma^2$ (not $\sigma$ ) and setting the derivative to zero leads to the maximum likelihood estimator
$$\hat{\sigma^2}=\operatorname{RSS}(\hat{\boldsymbol{\beta}}) / n .$$
This estimator happens to be brased, but the bias is easily corrected dividing by $n-p$ instead of $n$. The situation is exactly analogous to the use of $n-1$ instead of $n$ when estimating a variance. In fact, the estimator of $\sigma^2$ for the null model is the sample variance, since $\hat{\beta}=\bar{y}$ and the residual sum of squares is $\operatorname{RSS}=\sum\left(y_i-\bar{y}\right)^2$.

Under the assumption of normality, the ratio RSS $/ \sigma^2$ of the residual sum of squares to the true parameter value has a chi-squared distribution with $n-p$ degrees of freedom and is independent of the estimator of the linear parameters. You might be interested to know that using the chi-squared distribution as a likelihood to estimate $\sigma^2$ (instead of the normal likelihood to estimate both $\boldsymbol{\beta}$ and $\sigma^2$ ) leads to the unbiased estimator.

For the sample data the RSS for the null model is $2650.2$ on 19 d.f. and therefore $\hat{\sigma}=11.81$, the sample standard deviation.

# 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|Properties of the Estimator

$$\mathrm{E}(\hat{\boldsymbol{\beta}})=\boldsymbol{\beta} .$$

$$\operatorname{var}(\hat{\boldsymbol{\beta}})=\left(\mathbf{X}^{\prime} \mathbf{X}\right)^{-1} \sigma^2$$

$$\hat{\boldsymbol{\beta}} \sim N_p\left(\boldsymbol{\beta},\left(\mathbf{X}^{\prime} \mathbf{X}\right)^{-1} \sigma^2\right)$$

## 统计代写|广义线性模型代写generalized linear model代考|Estimation of 2

$$\log L\left(\sigma^2\right)=-\frac{n}{2} \log \left(2 \pi \sigma^2\right)-\frac{1}{2} \operatorname{RSS}(\hat{\boldsymbol{\beta}}) / \sigma^2 .$$

$$\hat{\sigma^2}=\operatorname{RSS}(\hat{\boldsymbol{\beta}}) / n .$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。