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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Jackknife

The jackknife estimate of variance estimates variability in fitted parameters by comparing results from leaving out one observation at a time in repeated estimations. Jackknifing 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. Subsample estimates are collected and compared with the full sample estimate to assess variability. Introduced by Quenouille (1949) , an excellent review of this technique and extensions is available in $\underline{\text { Miller (1974). }}$
The sandwich estimate of variance is related to the jackknife.
Asymptotically, it is equivalent to the one-step and iterated jackknife estimates, and as shown in Efron (1981), the sandwich estimate of variance is equal to the infinitesimal jackknife.
There are two general methods for calculating jackknife estimates of variance. One approach is to calculate the variability of the individual estimates from the full sample estimate. We supply formulas for this approach. A less conservative approach is to calculate the variability of the individual estimates from the average of the individual estimates. You may see references to this approach in other sources. Generally, we prefer the approach outlined here because of its more conservative nature.

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

A jackknife estimate of variance may be formed by estimating the coefficient vector for $n-1$ observations $n$ different times. Each of the $n$ estimates is obtained for a different subsample of the original dataset. The first estimate considers a subsample where only the first observation is deleted from consideration. The second estimate considers a subsample where only the second observation is deleted from consideration. The process continues until $n$ different subsamples have been processed and the $n$ different estimated coefficient vectors are collected. The $n$ estimates are then used as a sample from which the variability of the estimated coefficient vector may be estimated.
The jackknife estimate of variance is then calculated as
$$\widehat{V}{J}=\frac{n-p}{n} \sum{i=1}^{n}\left(\widehat{\boldsymbol{\beta}}{(i)}-\widehat{\boldsymbol{\beta}}\right)\left(\widehat{\boldsymbol{\beta}}{(i)}-\widehat{\boldsymbol{\beta}}\right)^{T}$$
where $\widehat{\beta}_{(i)}$ is the estimated coefficient vector leaving out the $i$ th observation 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|Usual jackknife

$$\widehat{V} J=\frac{n-p}{n} \sum i=1^{n}(\widehat{\boldsymbol{\beta}}(i)-\widehat{\boldsymbol{\beta}})(\widehat{\boldsymbol{\beta}}(i)-\widehat{\boldsymbol{\beta}})^{T}$$

## 有限元方法代写

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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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