assignmentutor-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|Bootstrap

The bootstrap estimate of variance is based on a data resampling procedure in which the variability of an estimator is investigated by repeating an estimation with a subsample of the data. Subsamples are drawn with replacement from the original sample and subset estimates are collected and compared with the full (original) sample estimate.

There are two major types of bootstrap variance estimates: parametric and nonparametric. Parametric bootstrap variance estimates are obtained by resampling residuals, and nonparametric variance estimates are obtained by resampling cases (as described above). Because the provided software calculates nonparametric bootstrap variance estimates, we describe that in the following subsections.

Within the area of nonparametric bootstrap variance estimates, one may calculate the variability about the mean of the bootstrap samples or about the original estimated coefficient vector. The former is more common, but we generally use (and have implemented) the latter because it is more conservative.
As a note of caution: applying this variance estimate usually calls for art as well as science because there are usually some bootstrap samples that are “bad”. They are bad in the sense that estimation results in infinite coefficients, collinearity, or some other difficulty in obtaining estimates. When those conditions are known, they can be specified in the reject ( ) option of the bootstrap command, which will cause all results to be set to missing for that bootstrap sample.

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

A grouped (or cluster) bootstrap estimate of variance may be formed by estimating the coefficient vector $k$ different times. Each of the $k$ estimates is obtained by fitting the model using a random sample of the $g$ groups of observations (sampled with replacement) drawn from the original sample dataset of $g$ groups. The sample drawn for the groups in this case is such that all members of a group are included in the sample if the group is selected.
A bootstrap estimate of variance is then calculated as
$$\widehat{V}{\mathrm{GBS}}=\frac{n-p}{n k} \sum{i=1}^{k}\left(\widehat{\boldsymbol{\beta}}{i}^{\mathrm{GBS}}-\widehat{\boldsymbol{\beta}}\right)\left(\widehat{\boldsymbol{\beta}}{i}^{\mathrm{GBS}}-\widehat{\boldsymbol{\beta}}\right)^{T}$$
where $\widehat{\boldsymbol{\beta}}_{i}^{\mathrm{GBS}}$ is the estimated coefficient vector for the $i$ th sample (drawn with replacement) and $p$ is the number of predictors (possibly including a constant).

# 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|Grouped bootstrap

$$\widehat{V G B S}=\frac{n-p}{n k} \sum i=1^{k}\left(\widehat{\boldsymbol{\beta}} i^{\mathrm{GBS}}-\widehat{\boldsymbol{\beta}}\right)\left(\widehat{\boldsymbol{\beta}} i^{\mathrm{GBS}}-\widehat{\boldsymbol{\beta}}\right)^{T}$$

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## 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。

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