统计代写|多元统计分析代写Multivariate Statistical Analysis代考| Survival Data

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多元统计分析被认为是评估地球化学异常与任何单独变量和变量之间相互影响的意义的有用工具。

statistics-lab™ 为您的留学生涯保驾护航 在代写多元统计分析Multivariate Statistical 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。我们的专家在代写多元统计分析Multivariate Statistical Analysis代写方面经验极为丰富,各种代写多元统计分析Multivariate Statistical Analysis相关的作业也就用不着说。

我们提供的多元统计分析Multivariate Statistical Analysis及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 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|多元统计分析代写Multivariate Statistical Analysis代考| Survival Data

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Waiting Times

Survival Analysis is traditionally concerned with the waiting time to the occurrence of a particular event. This might be a terminal incident like death, as the name suggests, but more generally the event can be any well-defined circumstance. For example, the event can be some life-changing occurrence, such as cure from a disease, or winning the hand of the object of one’s desire, or finally convincing the editor that one’s paper should be accepted. Again, there is a whole parallel universe called reliability in which times to breakdown or repair of machines and systems is the subject of study. There are also numerous other applications including stimulus-response times, and performance times in sports and system operation. Response times, in particular, can have wide variation, for example, ranging from (a) a sprinter leaping into action after the starter’s gun to (b) a politician asked to apologise for wrecking the economy, education, employment, justice system, and so forth. The old saying, “You can wait till the cows come home” might apply to the latter.
The methods of Survival Analysis can be profitably applied to situations in which time is not time at all. For example, in studying construction materials it might be the breaking strength of a concrete block: one can think of the stress being increased steadily until the time of fracture. Again, it could be the level attained in some trial: imagine hanging in there as the test proceeds until you are forced to concede defeat. What such measurements do have in common is that they are positive, or at least non-negative, barring ingeniously contrived cases. Also, their values tend to have positively skewed distributions with long upper tails.

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Discrete Time

In most conventional applications of survival analysis the times are treated as being continuous, that is, measured on a continuous scale, and almost always as being positive or, at least, non-negative. This reflects a belief that time itself proceeds continuously, though not all physicists are convinced of this nowadays, apparently. Such philosophical puzzles need not detain us here-they just lead to sleepless nights. On the other hand, when one considers carefully the definition of failure time, there is plenty of room for consideration of discrete time.

Consider a system in continuous operation that is inspected periodically, manually or automatically. Suppose that, if one or more components are classified as effectively life-expired, the system is taken out of service for renewal or replacement. Then, discounting the possibility of breakdown between inspections, the lifetime of a component is the number of inspections to life expiration. Some systems have to respond to unpredictable demands, a typical task for standby units. A similar situation occurs when a continuously operating system is subject to shocks, peak stresses, loads, or demands. Such stresses can be regular, such as those caused by tides or weekly demand cycles, or irregular, as with storms and earthquakes. The lifetime for such a system is the number of shocks until failure.

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Censoring

In Survival Analysis it is almost obligatory to have to deal with censoring. The most common case is when the waiting time is right-censored. If this occurs at time $t$, it means that the actual time $T$ is only known to exceed $t$, that is, the extent of the information is that $T>t$. Such would arise, for example, with a machine still functioning at time $t$ when observation ceased at $5.30 \mathrm{p} . \mathrm{m}$. on Friday, or with an ex-offender still known to be on the right side of the law at time $t$ when probation monitoring finished. Another common example is with patients under observation after some treatment: they could be lost to follow-up if they moved away without leaving contact details and were consequently and subsequently lost sight of (“Mum, why do we always move at night?). Yet another situation is where a failure occurs for a reason different to the one under study. So, a patient might die of some condition other than the one being treated. In that case the analysis can be complicated by possible association between the different causes of failure-this is the subject of competing risks; see Part III.

Less common is left-censoring, when it is only known that failure occurred some time before observation was made. So, the game starts, then you need the loo, and when you return your mates are cheering because your team scored while you were indisposed. (Of course, you would probably have heard the roar of the crowd from your end of the stadium, but please overlook that slight flaw in the example. There is the legend of a supporter whose friends would plead with him to take a break; such was the observed association.) In medicine a left-censored time would result where the patient returns for

a scheduled check-up, which then indicates that the disease had recurred sometime previously. Formally, the information is just that $T<t$, where $t$ is observed.

Interval censoring means that the waiting time $T$ is not recorded precisely but can be bracketed, say between times $t_{1}$ and $t_{2}$. For example, the system might have crashed overnight, between leaving it running in the evening and returning to look at it the following morning. Again, the patient’s check-up at three months after treatment might show all clear, but then at six months indicate a recurrence of the disease sometime in between. Even recordings that are nominally exact might be just to the nearest minute or nearest day. It can be argued that virtually all survival times are interval censored, being rounded by the limited accuracy of the recording instrument. However, times are most often treated as exactly equal to the recorded value.

On the other hand, the starting time can also be subject to uncertainty. Switching on a machine is clear enough, but what about patients with chronic conditions? When someone is referred for medical treatment, and subsequently followed up, various initial times can be considered for the final analysis. Should it be when symptoms first appeared, relying on the memory of the patient? Should it be when the doctor was first consulted? Or when treatment started? Or when treatment finished? Such variations of definition can obviously have a significant effect on the analysis. It might be better to recognise that there is often a series of events, producing several inter-event times. Such multivariate data is the topic of Part II.

Survival Data 的图像结果
统计代写|多元统计分析代写Multivariate Statistical Analysis代考| Survival Data

多元统计分析代写

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Waiting Times

生存分析传统上关注特定事件发生的等待时间。顾名思义,这可能是像死亡一样的终末事件,但更一般地说,该事件可以是任何明确定义的情况。例如,事件可以是一些改变生活的事件,例如治愈疾病,或赢得心仪对象的手,或最终说服编辑相信自己的论文应该被接受。同样,存在一个称为可靠性的整个平行宇宙,其中机器和系统的故障或维修时间是研究的主题。还有许多其他应用,包括刺激响应时间,以及运动和系统操作中的性能时间。特别是响应时间可以有很大的变化,例如,从 (a) 一名短跑运动员在发令员的枪响后跳入行动,到 (b) 一名政客要求为破坏经济、教育、就业、司法系统等而道歉。“你可以等到奶牛回家”这句老话可能适用于后者。
生存分析的方法可以有效地应用于时间根本不是时间的情况。例如,在研究建筑材料时,它可能是混凝土砌块的断裂强度:人们可以认为应力稳定增加,直到断裂。再一次,它可能是在某些试验中达到的水平:想象在测试进行时一直呆在那里,直到你被迫承认失败。这些测量的共同点是它们是积极的,或者至少是非消极的,除非是巧妙设计的案例。此外,它们的值往往呈正偏态分布,上尾较长。

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Discrete Time

在大多数传统的生存分析应用中,时间被视为连续的,即以连续的尺度测量,并且几乎总是被视为正数,或者至少是非负数。这反映了一种信念,即时间本身是连续前进的,尽管现在显然并非所有物理学家都相信这一点。这样的哲学谜题不必把我们困在这里——它们只会导致不眠之夜。另一方面,当仔细考虑失效时间的定义时,有足够的空间来考虑离散时间。

考虑一个连续运行的系统,该系统定期、手动或自动地进行检查。假设,如果一个或多个组件被归类为有效寿命到期,系统将停止服务以进行更新或更换。然后,扣除检查之间发生故障的可能性,组件的寿命是寿命到期的检查次数。一些系统必须响应不可预测的需求,这是备用设备的典型任务。当连续运行的系统受到冲击、峰值应力、负载或需求时,也会出现类似的情况。这种压力可以是有规律的,例如由潮汐或每周需求周期引起的压力,也可以是不规则的,例如风暴和地震。这种系统的寿命是直到失效的冲击次数。

统计代写|多元统计分析代写Multivariate Statistical Analysis代考|Censoring

在生存分析中,几乎必须处理审查。最常见的情况是等待时间被右删失。如果这发生在时间吨, 表示实际时间吨只知道超过吨,即信息的范围是吨>吨. 例如,当机器仍在运行时,就会出现这种情况吨当观察停止时5.30p.米. 周五,或与当时仍处于法律右侧的前罪犯在一起吨当试用期监控结束时。另一个常见的例子是经过一些治疗后正在接受观察的患者:如果他们在没有留下联系方式的情况下离开,他们可能会失去随访,因此随后就被忽视了(“妈妈,为什么我们总是在晚上搬家?)。还有一种情况是由于与所研究的原因不同的原因而发生故障。因此,患者可能死于某些疾病而不是正在接受治疗的疾病。在这种情况下,由于不同故障原因之间可能存在关联,分析可能会变得复杂——这是竞争风险的主题;见第三部分。

不太常见的是左审查,当只知道失败发生在观察之前的某个时间时。所以,比赛开始了,然后你需要上厕所,当你回来时,你的队友会欢呼,因为你的球队在你身体不适的时候得分。(当然,你可能会从体育场的尽头听到人群的咆哮,但请忽略这个例子中的小瑕疵。有一个支持者的传说,他的朋友会恳求他休息一下;比如是观察到的关联。)在医学上,左删失时间将导致患者返回

定期检查,然后表明该疾病在以前的某个时间复发过。形式上,信息就是这样吨<吨, 在哪里吨被观察到。

间隔审查意味着等待时间吨没有精确记录,但可以括起来,比如在时间之间吨1和吨2. 例如,系统可能在一夜之间崩溃,在晚上让它运行到第二天早上返回查看它之间。再一次,患者在治疗后三个月的检查可能显示一切都清楚,但在六个月后表明疾病在两者之间的某个时间复发。即使是名义上精确的记录也可能只是到最近的分钟或最近的一天。可以说,几乎所有的生存时间都被间隔删失,被记录仪器的有限准确性四舍五入。但是,时间通常被视为与记录值完全相等。

另一方面,开始时间也可能存在不确定性。打开机器已经很清楚了,但是慢性病患者呢?当某人被转诊接受治疗并随后进行随访时,可以考虑各种初始时间进行最终分析。应该是在症状第一次出现的时候,依靠病人的记忆吗?应该是第一次咨询医生的时候吗?或者什么时候开始治疗?或者治疗结束的时候?这种定义的变化显然会对分析产生重大影响。最好认识到通常存在一系列事件,产生多个事件间时间。这种多元数据是第二部分的主题。

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金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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。

随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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