### 金融代写|金融计量经济学Financial Econometrics代考|The Contribution of Fuzzy Methods

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• 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

## 金融代写|金融计量经济学Financial Econometrics代考|Aspects of Information Quality

The view of information quality differs, depending on the domain of application and the kind of business generated by using data science (Batini and Scannapieco 2016 ; Capet and Revault d’Allonnes 2013; Revault d’Allonnes 2013; Lesot and Revault d’Allonnes 2017). The concept is complex and based on many components. Data quality has always been a major issue in databases, information systems and risk forecasting. The difficulties are accentuated in the environment of big data.

There exists a jungle of definitions of information quality and proposals of solutions in the framework of data mining and data science. In this jungle, you can recognize different aspects of information quality. Relevance, appropriateness, accessibility, compatibility correspond to the good matching between the retrieved information and the expectation of the user. Understandability, expressiveness describe the capacity of the system to speak a language familiar to the user. Accuracy may be necessary in specific domains. Comprehensibility, consistency, coherence, completeness represent the quality of the set of information as a whole. Timeliness, operationality, security are technical qualities. Veracity, validity, trust, reliability, plausibility, credibility represent various means to define the confidence the user can have in the obtained information.

The factors of information quality are dependent on the nature of sources, that can be news streams, databases, open source data, sensor records or social networks. They are also dependent on the form of data, for instance text, images, videos, temporal series, graphs or database records. The evaluation of information quality is also relatę to the expectations of the end usêr and purposes of the informaation extraction, the requirement of accuracy, for instance, not being the same in finance or for a student preparing a report.

To describe the chaining in the information quality components, we can consider that the source quality has an influence on data quality, which is one of the factors of information quality, as well as the artificial intelligence-based model quality. Finally, the user satisfaction rests on both information quality and model quality. To illustrate the diversity of views of information quality according to the considered domain of application, we can roughly consider that specialists of business intelligence and intelligence services pay a great attention to source quality, while financial engineering and econometrics are focused on data quality. In medical diagnosis, the quality of the model is very important to explain the diagnosis, whereas information retrieval is centered on global information quality. The user satisfaction is a priority for domains such as social networks or targeted advertising.

Each of these constituents of what we can call global information quality requires appropriate solutions to the best possible. In the following. we focus on fuzzy setbased solutions, among all those provided in computational intelligence, thanks to the capacity of fuzzy systems to handle uncertainties, imprecisions, incompleteness and reliability degrees in a common environment. The diversity of aggregation methods available for the fusion of elements and the richness of measures of similarity are additional reasons to choose fuzzy methods.

## 金融代写|金融计量经济学Financial Econometrics代考|Fuzzy Solutions to Data Quality Problems

Defaults in data quality are the most primary among the problems occurring in information quality, and they have been studied for years, in particular in databases (Ananthakrishna et al. 2002; Janta-Polczynski and Roventa 1999) and more generally in products and services (Loshin 2011). Accuracy, completeness and consistency of data (Huh et al. 1990) have always been major concerns in industrial products.
In the modern environments, it is necessary to have a more general approach of data quality and to consider both intrinsic and extrinsic factors. The former include defaults such as imprecision, measurement errors, vague linguistic descriptions, incompleteness, inaccuracies, inconsistencies and discrepancies in data elements. The latter mainly refers to insufficient trustworthiness of sources and inconsistency between various sources.

A general analysis of data quality is proposed in Pipino et al. (2002) by means of objective and subjective assessments of data quality. The question of measuring the quality of data is addressed in Bronselaer et al. $(2018 \mathrm{a}, \mathrm{b})$ through the presentation of a measure-theoretic foundation for data quality and the proposal of an operational measurement.

A fuzzy set-based knowledge representation is of course an interesting solution to the existence of inaccuracies, vagueness and incompleteness, as well as the necessary bridge between linguistic and numerical values.

The issue of incomplete data is very frequent in all environments, for various reasons such as the absence of answer to a specific request, the impossibility to obtain a measurement, a loss of pieces of information or the necessity to hide some elements to protect privacy. Various fuzzy methods have been proposed for the imputation of missing values, based on very different techniques. For instance, a fuzzy K-means clustering algorithm is used in Liao et al. (2009) and Li et al. (2004), a neuro-fuzzy $\mathrm{~ c l a ̊ s s i f i e r ~ i s ~ p r o p o ̄ o s e d ~ i n ~ G a ̉ b r y s ~ ( 2 0 0 2 ) , ~ e v o o l u t i o n a ̆ r y ~ f u z z y ~ s o ̄ l u t i o n s ~ a a r e ~ i n v e}$ in Carmona et al. (2012). Rough fuzzy sets are incorporated in a neuro-fuzzy structure to cope with missing data in Nowicki (2009). Various types of fuzzy rule-based classification systems are studied in Luengo et al. (2012) to overcome the problem of missing data. Missing pixels in images are also managed by means of fuzzy methods, for instance with the help of an intuitionistic fuzzy C-means clustering algorithm in Balasubramaniam and Ananthi (2016). All these works exemplify the variety of solutions available in a fuzzy setting.

## 金融代写|金融计量经济学Financial Econometrics代考|Fuzzy Approaches to Other Information Quality

Information is extracted from data by means of an artificial intelligence engine and it is supposed to fulfill users’ needs. The expected level of quality of information is different, depending on the purpose of information extraction. In information retrieval, the challenge is to find images, texts or videos corresponding to a user’s query and the information quality reflects the adequacy and completeness of the obtained information. In domains such as prevision, prediction, risk or trend forecasting, the quality of information is evaluated on the basis of the comparison between the forecasting and the real world. In real time decision making, where a diagnosis or a solution to a problem must be provided rapidly, the completeness and accuracy of information is crucial. In cases where data must be analyzed instantaneously to act on a system, for instance in targeted advertising, adaptive interfaces or online sales, the timeliness and operationality are more important than the accuracy. In business intelligence or e-reputation analysis, where opinions, blogs or customer’s evaluations are analyzed, the trustworthiness of information is crucial.

We propose to structure the analysis of information quality along three main dimensions, namely the relevance of information, its trust or veracity, and its understandability.

## 金融代写|金融计量经济学Financial Econometrics代考|Fuzzy Solutions to Data Quality Problems

Pipino 等人提出了对数据质量的一般分析。（2002 年）通过对数据质量的客观和主观评估。Bronselaer 等人解决了衡量数据质量的问题。(2018一个,b)通过介绍数据质量的测量理论基础和操作测量的建议。

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## MATLAB代写

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