### 统计代写|贝叶斯分析代写Bayesian Analysis代考|Time Series and Regression

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯分析Bayesian Analysis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。我们的专家在代写贝叶斯分析Bayesian Analysis代写方面经验极为丰富，各种代写贝叶斯分析Bayesian Analysis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|贝叶斯分析代写Bayesian Analysis代考|Time Series and Regression

The first time series to be presented is the linear regression model with AR (1) errors, where $\mathrm{R}$ is employed to generate observations from that linear model with known parameters followed by an execution with WinBUGS that produces point and interval estimates of those parameters. The scenario is repeated for a quadratic regression with seasonal effects and AR(1) errors. In all scenarios, $R$ code generates the data and using that as information for the Bayesian analysis, the posterior distribution of the parameters is easily provided. Various generalizations to more complicated situations include a nonlinear regression model (with exponential trend) with AR(1) errors, a linear regression model with AR(2) errors which contains five parameters, two linear regression coefficients, two autoregressive parameters, plus the precision of the error terms. An interesting time series model is one which is continuous time, which is the solution to a stochastic differential equation, where the posterior distribution of the autoregressive parameter is derived assuming a noninformative prior distribution for the autoregressive parameter and the variance of the errors. The chapter concludes with a section on comments and conclusions followed by eight problems and eight references.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Time Series and Stationarity

Stationarity of a time series is defined including stationary in the mean and stationary in the variance. The first time series to be considered is the moving average model and the first and second moments are derived. It is important to note that moving average processes are always stationary. $R$ is employed to generate observations from an MA(1) series with known parameters (the moving average coefficient and the precision of the white noise); then using those observations, WinBUGS is executed for the posterior analysis which provides point and credible intervals for the two parameters. The MA(1) series is generalized to quadratic regression model with MA(1) errors, where again $\mathrm{R}$ generates observations from it where the parameters are known.

Bayesian analysis via WinBUGS performs the posterior analysis. An additional generalization focuses on a quadratic regression model with an MA(2) series for the errors with $R$ used to generate observations from the model but with known values for the parameters. It is interesting to compare the results of the posterior estimates of the quadratic regression with MA(1) errors to that with MA(1) errors. Generalizing to a regression model with a quadratic trend, but also including seasonal effects, presents a challenge to the Bayesian way of determining the posterior analysis. The predictive density for forecasting future observations is derived, and the last section of the chapter presents the Bayesian technique of testing hypotheses about the moving average parameters. There are 16 exercises and 5 references.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Time Series and Spectral Analysis

It is explained how spectral analysis gives an alternative approach to studying time series, where the emphasis is on the frequency of the series, instead of the time. Chapter 7 continues with a brief history of the role of the spectral density function in time series analysis, and the review includes an introduction to time series with trigonometric components (sine and cosine functions) as independent variables. Next to be explained are time series with trigonometric components whose coefficients are Fourier frequencies and their posterior distributions are revealed with a Bayesian analysis. It is important to remember that frequency is measured in hertz (one cycle per second) or in periods measured as so many units of pi radians per unit time. It can be shown that for the basic time series (autoregressive and moving average series), the spectral density function is known in terms of the parameters of the corresponding model. It is important to remember the spectrum is related to the Fourier line spectrum which is a plot of the fundamental frequencies versus $m$, where $m$ is the number of harmonics in the series. Section 10 continues with the derivation of the spectral density function for the basic series, AR(1), AR (2), MA(1), MA(2), ARMA(1,1), etc. Remember that the spectral density function is a function of frequency (measured in hertz or multiples of $\mathrm{pi}$ in radians per cycle). For each model, $\mathrm{R}$ is used to generate observations from that model (with known parameters); then, using those observations as data, the Bayesian analysis is executed with WinBUGS and as a consequence the posterior distribution of the spectral density at various frequencies is estimated. For the last example, the spectral density of the sunspot cycle is estimated via a Bayesian analysis executed with WinBUGS. There are 17 problems followed by $y$ references.

## 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。