金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

如果你也在 怎样代写金融计量经济学Financial Econometrics这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。

金融计量学是将统计方法应用于金融市场数据。金融计量学是金融经济学的一个分支,在经济学领域。研究领域包括资本市场、金融机构、公司财务和公司治理。

statistics-lab™ 为您的留学生涯保驾护航 在代写金融计量经济学Financial Econometrics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。我们的专家在代写金融计量经济学Financial Econometrics代写方面经验极为丰富,各种代写金融计量经济学Financial Econometrics相关的作业也就用不着说。

我们提供的金融计量经济学Financial Econometrics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
金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

This aspect corresponds to the adequacy of the retrieved information provided to the user with his/her query, or more generally his/her needs or expectations. This issue has extensively been analyzed and many kinds of solutions have been proposed in a fuzzy setting, as described in Zadeh’s paper (2006), which pointed out the various aspects of relevance in the semantic web, in particular topic relevance, question relevance and the consideration of perception-based information. Fuzzy compatibility measures are investigated in Cross (1994) to evaluate the relevance in information retrieval.

Fuzzy formal concept analysis brings efficient solutions to the representation of information in order to retrieve relevant information (Medina et al. 2009; Lai and Zhang 2009; De Maio et al. 2012). Fuzzy ontologies are also used to improve relevance, as described in Calegari and Sanchez (2007), Akinribido et al. (2011) and their automatic generation is studied in Tho et al. (2006). Fuzzy or possibilistic description logic can also be used (Straccia 1998; Straccia 2006; Qi et al. 2007) to facilitate the identification of significant elements of information to answer a query and to avoid inconsistencies (Couchariere et al. 2008; Lesot et al. 2008). Fuzzy clustering was also used to take into account relevance in text categorization (Lee and Jiang 2014).

The pertinence of images retrieved to satisfy a user query has particularly attracted the attention of researchers. Similarity measures may be properly chosen to achieve

a satisfying relevance (Zhao et al. 2003; Omhover and Detyniecki 2004). Fuzzy graph models are presented in Krishnapuram et al. (2004) to enable a matching algorithm to compare the model of the image to the model of the query. Machine learning methods are often used to improve the relevance, for instance active learning requesting the participation of the user in Chowdhury et al. (2012) or semi-supervised fuzzy clustering performing a meaningful categorization that will help image retrieval to be more relevant (Grira et al. 2005). Concept learning is performed thanks to fuzzy clustering in order to take advantage of past experiences (Bhanu and Dong 2002). The concept of fuzzy relevance is also addressed (Yap and Wu 2003) and linguistic relevance is explored (Yager and Petry 2005) to take into account perceptual subjectivity and human-like approach of relevance.

All these methods are representative of attempts to improve the relevance of the information obtained by a retrieval system to satisfy the user’s needs, based on a fuzzy set-based representation.

金融代写|金融计量经济学Financial Econometrics代考|Trust or Veracity of Information

The trustworthiness of information is crucial for all domains where users look for information. A solution to take this factor of quality into account lies in the definition of a degree of confidence attached to a piece of information (Lesot and Revault d’Allonnes 2017) to evaluate the uncertainty it carries and the confidence the user can have in it. We focus here on fuzzy set-based or possibilistic approaches.

First of all, the sources of information have a clear influence on the user’s trust of information (Revault d’Allonnes 2014), because of their own reliability mainly based on their importance and their reputation. Their competence on the subject of the piece of information is another element involved in the trust of information, for instance the Financial Times is more renowned and expert in economics than the Daily Mirror. The relevance of the source with respect to the event is an additional component of the user’s trust in a piece of information, be the relevance geographical or relating to the topic of the event. For instance, a local website such as wildfiretoday.com may be more relevant to obtain precise and updated information on bushfires than wellknown international media such as $B B C$ News. Moreover, a subjective uncertainty expressed by the source, such as “We believe” or “it seems”, is an element of the trustworthiness of the source.

The content of the piece of information about an event also bears a part of uncertainty environ is inherent in the formulation itself through numerical imprecisions (“around 150 persons died” or “between 1000 and 1200 cases of infection”) or symbolic ones (“many participants”). Linguistic descriptions of uncertainty can also be present “”probably”, “almost certainly”, ” 69 homes believed destroyed”, “2 will probably survive”). Uncertain information can also be the consequence of an insufficient compatibility between several pieces of information on the same event. A fuzzy set-based knowledge representation contributes to taking into account imprecisions and to evaluating the compatibility between several descriptions such as «more than 70» and «approximately 75». The large range of aggregation methods in a fuzzy setting helps to achieve the fusion of pieces of information on a given event in order to confirm or invalidate each of them through a comparison with others, and to therefore overcome compatibility problems.

金融代写|金融计量经济学Financial Econometrics代考|Understandability of Information

The last component of the quality of information content we consider is its understandability or expressiveness. It is a complex notion (Marsala and Bouchon-Meunier 2015; Hüllermeier 2015), dealing with the understandability of the process leading to the presented information, as well as the easiness for the end user to interpret the piece of information he receives. This component has been widely investigated since the introduction of Explainable Artificial Intelligence (XAI) by DARPA in 2016 (https://www.darpa.mil/program/explainable-artificial-intelligence), that requires an explainable model and an explanation interface.

Fuzzy models are recognized for their capability to be understood. In particular, fuzzy rule-based systems are considered to be easily understandable because rules of the form “If the current return is Low or the current return is High, then low or high future returns are rather likely” (Van den Berg et al. 2004) contain symbolic descriptions similar to what specialists express. Fuzzy decision trees (Laurent et al. 2003; Bouchon-Meunier and Marsala 1999) are also very efficient models of the reasons why a conclusion is presented to the user. Nevertheless, a balance between complexity, accuracy and understandability of such fuzzy models is necessary (Casillas et al. 2003). The capacity of the user to understand the system represented by the fuzzy model depends not only on the semantic interpretability induced by natural-language like descriptions, but also on the number of attributes involved in premises and the number of rules (Gacto et al. 2011). The interpretability of a fuzzy model is a subjective appreciation and it is possible to distinguish highlevel criteria such as compactness, completeness, consistency and transparency of fuzzy rules, from low-level criteria such as coverage, normality or distinguishability of fuzzy modalities (Zhou and Gan 2008).

The interpretability of information presented to the user depends on the expertise of the user. Linguistic descriptions are not the only expected form of information extracted from time series or large databases, for instance. Formal logic, statistics or graphs may look appealing to experts. However, we focus here on fuzzy methods that provide linguistic information by means of fuzzy modalities such as “rapid increase” or “low cost” and fuzzy quantifiers like “a large majority” or “very few”. The wide range of works on linguistic summarization of big databases and time series by means of fuzzy descriptions and so-called protoforms (Zadeh 2002) shows the importance of the topic, starting from seminal definitions of linguistic summaries (Yager 1982; Kacprzyk and Yager 2001). Their interpretability can be questioned (Lesot et al. 2016) and improved by means of automatic methods, such as mathematical morphology (Moyse et al. 2013) or evolutionary computation (Altintop et al. 2017 ), among other methods.

金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

金融计量经济学代考

金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

这个方面对应于提供给用户的检索信息的充分性,用户的查询,或更一般地,他/她的需要或期望。正如 Zadeh 的论文 (2006) 所述,该问题已被广泛分析,并在模糊设置中提出了多种解决方案,该论文指出了语义网络中相关性的各个方面,特别是主题相关性、问题相关性和考虑基于感知的信息。Cross (1994) 研究了模糊兼容性度量,以评估信息检索中的相关性。

模糊形式概念分析为信息的表示带来了有效的解决方案,以便检索相关信息(Medina et al. 2009; Lai and Zhang 2009; De Maio et al. 2012)。如 Calegari 和 Sanchez (2007)、Akinribido 等人所述,模糊本体也用于提高相关性。(2011)及其自动生成在 Tho 等人中进行了研究。(2006 年)。也可以使用模糊或可能的描述逻辑(Straccia 1998;Straccia 2006;Qi 等人 2007)来帮助识别重要的信息元素以回答查询并避免不一致(Couchariere 等人 2008;Lesot 等人。 2008)。模糊聚类也用于考虑文本分类中的相关性(Lee and Jiang 2014)。

为满足用户查询而检索到的图像的相关性尤其引起了研究人员的关注。可以适当地选择相似性度量来实现

令人满意的相关性(Zhao et al. 2003; Omhover and Detyniecki 2004)。Krishnapuram 等人提出了模糊图模型。(2004)使匹配算法能够将图像模型与查询模型进行比较。机器学习方法通​​常用于提高相关性,例如在 Chowdhury 等人中要求用户参与的主动学习。(2012)或半监督模糊聚类执行有意义的分类,这将有助于图像检索更加相关(Grira et al. 2005)。概念学习是通过模糊聚类来执行的,以便利用过去的经验(Bhanu 和 Dong 2002)。

所有这些方法都代表了基于基于模糊集的表示来提高检索系统获得的信息的相关性以满足用户需求的尝试。

金融代写|金融计量经济学Financial Econometrics代考|Trust or Veracity of Information

信息的可信度对于用户查找信息的所有域都至关重要。将这一质量因素考虑在内的解决方案在于定义附加到一条信息的置信度(Lesot 和 Revault d’Allonnes 2017),以评估它所携带的不确定性以及用户对它的置信度。我们在这里专注于基于模糊集或可能的方法。

首先,信息来源对用户对信息的信任有明显的影响(Revault d’Allonnes 2014),因为它们自身的可靠性主要取决于它们的重要性和声誉。他们在信息主题上的能力是信息信任的另一个要素,例如,金融时报比每日镜报更知名,更擅长经济学。源与事件的相关性是用户对一条信息的信任的附加组成部分,可以是地理相关性或与事件主题相关的相关性。例如,wildfiretoday.com 等本地网站可能比知名国际媒体(如乙乙C消息。此外,来源表达的主观不确定性,例如“我们相信”或“似乎”,是来源可信度的一个要素。

关于事件的信息的内容也带有部分不确定性,环境是公式本身固有的数字不精确性(“大约 150 人死亡”或“1000 到 1200 例感染病例”)或象征性的(“许多参与者”)。不确定性的语言描述也可以出现““可能”、“几乎可以肯定”、“69 个房屋被认为被毁”、“2 个可能会幸存”)。不确定的信息也可能是同一事件的几条信息之间兼容性不足的结果。基于模糊集的知识表示有助于考虑不精确性并评估几种描述之间的兼容性,例如“超过 70”和“大约 75”。

金融代写|金融计量经济学Financial Econometrics代考|Understandability of Information

我们考虑的信息内容质量的最后一个组成部分是它的可理解性或表达性。这是一个复杂的概念(Marsala 和 Bouchon-Meunier 2015;Hüllermeier 2015),涉及导致呈现信息的过程的可理解性,以及最终用户解释他收到的信息的难易程度。自 DARPA 于 2016 年(https://www.darpa.mil/program/explainable-artificial-intelligence)引入可解释人工智能 (XAI) 以来,该组件已被广泛研究,这需要可解释的模型和解释界面。

模糊模型因其被理解的能力而被认可。特别是,基于模糊规则的系统被认为很容易理解,因为“如果当前回报低或当前回报高,那么未来回报低或高的可能性很大”形式的规则(Van den Berg et al. 2004)包含类似于专家表达的符号描述。模糊决策树(Laurent 等人 2003;Bouchon-Meunier 和 Marsala 1999)也是向用户呈现结论的原因的非常有效的模型。然而,这种模糊模型的复杂性、准确性和可理解性之间的平衡是必要的(Casillas et al. 2003)。用户理解模糊模型所代表的系统的能力不仅取决于类似自然语言的描述所引起的语义可解释性,还涉及前提中涉及的属性数量和规则数量(Gacto et al. 2011)。模糊模型的可解释性是一种主观评价,可以将模糊规则的紧凑性、完整性、一致性和透明度等高级标准与模糊模态的覆盖率、正态性或可区分性等低级标准区分开来(Zhou 和 Gan 2008)。

呈现给用户的信息的可解释性取决于用户的专业知识。例如,语言描述并不是从时间序列或大型数据库中提取的唯一预期信息形式。形式逻辑、统计数据或图表可能看起来对专家很有吸引力。然而,我们在这里关注的是通过“快速增加”或“低成本”等模糊模式和“绝大多数”或“很少”等模糊量词来提供语言信息的模糊方法。通过模糊描述和所谓的原型(Zadeh 2002)对大型数据库和时间序列进行语言总结的广泛工作表明了该主题的重要性,从语言总结的开创性定义开始(Yager 1982;Kacprzyk 和 Yager 2001 )。它们的可解释性可能会受到质疑(Lesot et al.

金融代写|金融计量经济学Financial Econometrics代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

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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