Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

Outlier Detection and a Method of Adjustment for the Iranian Manufacturing Establishment Survey Data

The role and importance of the industrial sector in the economic development necessitate the need to collect and to analyze accurate and timely data for exact planning. As the occurrence of outliers in establishment surveys are common due to the structure of the economy, the evaluation of survey data by identifying and investigating outliers, prior to the release of data, is necessary. In this paper, different robust multivariate outlier detection methods based on the Mahalanobis distance with blocked adaptive computationally efficient outlier nominators algorithm, minimum volume ellipsoid estimator, minimum covariance determinant estimator and Stahel-Donoho estimator are used in the context of a real dataset. Also some univariate outlier detection methods such as Hadi and Simonoff’s method, and Hidiroglou-Barthelot’s method for periodic manufacturing surveys are applied. The real data set is extracted from the Iranian Manufacturing Establishment Survey. These data are collected each year by the Statistical Center of Iran using sampling weights. In this paper, in addition to comparing different multivariate and univariate robust outlier detection methods, a new empirical method for reducing the effect of outliers based on the value modification method is introduced and applied on some important variables such as input and output. In this paper, a new four-step algorithm is introduced to adjust the input and output values of the manufacturing establishments which are under-reported or over-reported. A simulation study for investigating the performance of our method is also presented

Business Cycle Analysis with Multivariate Markov Switching Models

The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of a common latent factor. In the second approach a VAR model with parameters depending on one common Markov chain is considered (MSVAR). We will extend the MSVAR approach allowing for the presence of specific Markov chains in each equation of the VAR (MMSVAR). In the MMSVAR approach we also explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which allow a more parsimonious representation. We apply our model to study the relationship between cyclical phases of the industrial production in the US and Euro zone. Moreover, we construct a MMS model to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index.Economic cycles, Multivariate models, Markov switching models, Common latent factors, Causality, Euro-zone

Board Intellectual Capital, Board Effectiveness and Corporate Performance: Goodness of the Data

Many factors influence corporate performance and among them, intellectual capital (IC) and corporate governance are the most important determinants. Based on the literature, the direct effect of IC and corporate governance mechanisms on corporate performance have been measured in the past several years. Nevertheless, to empirically test indirect effect of board IC and board effectiveness on corporate performance remains scant. In addition, most of the research in these areas have been conducted in developed countries. It is found that not much research has been carried out in the emerging markets of Middle-East like Iran. The purpose of this paper is to present goodness of data processes in relation to study board IC, board effectiveness and corporate performance of listed companies in Iran. The goodness of data involves screening and purifying of raw data in accordance with the assumptions of multivariate analysis. Data screening is the process of checking data for errors and correcting them before performing data analysis. The study employed census method where all listed companies in Tehran Stock Exchange (TSE) were investigated. Data were obtained through the questionnaire survey on 292 board members in TSE. Raw data were keyed into Statistical Package for Social Science (SPSS) version 22. A descriptive statistic, treatment of missing data, univariate assessment and removing of outliers, normality and multicollinearity tests were conducted. The results from data cleaning revealed a significance and the suitability of the data for multivariate analysis

A Predictive Study on Instructional Design Quality, Learner Satisfaction and Continuance Learning Intention with E-learning Courses: Data Screening and Preliminary Analysis

As E-learning initiatives are increasingly being deployed in educational and corporate training settings to revamp work-place productivity through life-long learning, concerns related to instructional design quality among stakeholders are equally growing. Thus, the overriding objective of the study was to carry out initial screening and preliminary analysis of the data related to the causal influence of instructional design quality on learner satisfaction and continuance learning intention. Based on the survey design, the quantitative data were collected from 837 students across ten CISCO Networking academies in Uganda. Descriptive statistics, multiple regression and factor analysis techniques were employed to address the purpose of the study. Primary attention was paid to the assumptions of response rate, missing data, outliers, data normality, multicollinearity, homoscedasticity and common method bias. The results of the initial screening and preliminary data analysis revealed non violation of prerequisite multivariate assumptions. The findings have provided empirical evidence on the psychometric study of which the instrument can be further used for future research. The steps taken for the analysis have provided a benchmark of audit trail in the methodology and statistical analysis for the replication of the stud