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The objective of this study is to propose a functional framework for hydrological applications by treating flood hydrographs as functional data. Discrete flow data are transformed into a smoothing hydrograph curve, which can be analysed at any time interval. The concept of functional data considered the entire curve concerning time as a single observation. This chapter briefly discussed the idea of descriptive statistics, principal components and outliers in a functional framework. These methods were illustrated in the flood study at Sungai Kelantan River Basin, Malaysia. The results showed that five main components accounted for almost 73.8% of the overall flow variance. Based on the results of the factor scores, the hydrograph curves for the years 1988, 1993 and 2014 may be said to have a unique cluster of their own, while the rest of the years which consider having the same pattern. Due to various shapes and magnitudes, the hydrograph curves of 1988 and 2014 are considered outliers. In conclusion, the functional framework has shown that it is capable of representing a wide range of hydrographs and is capable of extracting additional information found in the hydrograph curve that cannot possibly be captured using classical statistical methods.

The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA).

The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors.

Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting.

The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue.

To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

This study aims to investigate the extent and pattern of the influence of one of the most important decision-making tools in the context of social commerce. This study demonstrates how much customer testimonials (including verified purchases and ordinary users) can influence the sales rank of experience and search goods.

The data were collected by text mining and performing a content analysis on the XML documents of Web pages and processing them. For search goods, 22,311 opinions were recorded regarding 95 mobile phones. Additionally, for experience goods, 67,817 opinions were recorded regarding 162 books in the Amazon online store. The data were analyzed by functional regression method in longitudinal data analysis.

In terms of importance, the opinions and recommendations of verified purchases had a 60% greater impact on the sales rank of experience goods than the opinions and recommendations of ordinary users. In search goods, the opinions of ordinary users had a greater impact than the opinions of verified purchases. The historical effect of the opinions of ordinary users at the end of the review period on sales rank was evident, while the historical effect of the verified purchase viewpoints during the review period had a nonlinear curve. The results showed that it was necessary to increase the volume of comments to increase their reliability in experience goods.

Measuring the effect of customer testimonials helps the managers of retail websites design algorithms and online suggestion systems, thereby improving the sales of their products by providing information desired by customers.

Individuals can be a source of information and influence the buying decision process of others by sharing their experiences. This issue helps reduce the purchase risk and explains the importance of interaction and sharing the customer’s experience.

Analyzing the impact of customer testimonials by separating verified purchases and ordinary users is one of the advantages of this study. The quantitative estimation of the impact of recommendations and the provision of a model of their historical effect is one of the approaches not addressed in similar studies.

Functional assessment-based interventions are a tertiary support that have been incorporated in many three-tiered models of prevention to support students who do not respond to more global prevention efforts. Although endorsed by host of reputable organizations (e.g., National Association of School Psychologists) and mandated in Individuals with Disabilities Education Act (IDEA, 1997, 2004), concerns have been raised that this mandate may not be warranted if functional assessment-based interventions do not meet minimum criteria to establish this as an evidence-based practice. One issue contributing to this concern is variability in the functional assessment process. John Umbreit and colleagues (2007) have attempted to address this concern by introducing a systematic approach that includes (a) a Function Matrix to analyze functional assessment data and identify the hypothesized function(s) of the target behavior and (b) a Function-Based Intervention Decision Model to guide intervention planning. In this chapter, we applied the core quality indicators for single-case research developed by Horner, Carr, Halle, McGee, Odom, and Wolery (2005) to studies conducted using this practice to determine the extent to which this systematic approach to functional assessment-based interventions met the standards for evidence-based practices for use in educational settings across the K-12 continuum for students with or at-risk for high incidence disabilities. If this practice is deemed to meet criteria, then this systematic approach may be particularly useful in meeting the mandate established in IDEA. Results suggest that it may be appropriate to establish this systematic method as a promising practice.

The purpose of this paper is to discuss the usefulness of Halliday’s linguistic theory known as Systemic Functional Linguistics (SFL) in analysing qualitative data. In order to do this, it initially presents an overview of SFL, and then explains how and why four linguistic features namely, nominalisation, grammatical metaphor, thematic structure and lexical density are useful in examining qualitative data. The paper also discusses three social metafunctions of language known as the ideational, the interpersonal and the textual metafunctions which are significant for understanding and interpreting texts.

This paper employs SFL as the main theoretical framework to discussing the usefulness of this linguistics theory in qualitative data analysis.

SFL can be seen as a paradigm shift in linguistic theory moving away from the traditional focus on syntax to the inclusion of the interface between language and pragmatics. The focus of SFL is language in use. It deals with texts in social contexts, which is the main focus in qualitative data analysis. Thus, SFL provides both research tools and theoretical insights for understanding and interpreting texts.

This paper provides significant insights into language which are crucial for understanding and interpreting texts in social contexts.

This article describes the University of Iowa’s process of system migration from selection and data conversion to implementation and presentation of this new system to staff, faculty, students, and the public. Following a three‐year selection effort, Ex Libris’ Aleph500 was chosen as the new system. Staff effort during the selection process and implementation is analyzed and quantified. A comprehensive review of implementation efforts is described including system and client configuration, functional testing and problem reporting, training, and local programming.

Increasing awareness of functional foods would have many health benefits such as reducing the incidence of non communicable diseases. The aim of this study is to investigate consumer awareness and consumption of functional foods in the city of Thessaloniki, Greece.

A sample population of consumers was randomly selected outside popular supermarkets in the city of Thessaloniki (n=154). Trained interviewers conducted interviews and a questionnaire was completed by each participant. Socio‐demographic information and details of knowledge and consumption of functional foods were obtained. Data were analyzed using Stata.

The analysis of the data showed that only 33 per cent of the consumers were aware of the term “functional foods”. Interestingly, the proportion of the sample population that knew about foods with health promoting factors was over 95 per cent. The term “functional food” was unfamiliar to the sample population. Over 70 per cent of the consumers surveyed consumed such foods, unaware of the terminology.

This appears to be the first Greek study to examine consumer awareness and consumption of functional foods.

To present research in the area of the applications of modern heuristics and data mining techniques in knowledge discovery.

Applications of data mining for neural networks using NeuralWare Predict^® software, genetic algorithms using Biodiscovery GeneSight^® (2005) software, and regression and discriminant analysis using SPSS^® were selected for bioscience data sets of continuous numerical‐valued Abalone fish data and discrete nominal‐valued mushroom data.

This paper illustrates the useful information that can be obtained using data mining for evolutionary algorithms specifically as those for neural networks, genetic algorithms, regression analysis, and discriminant analysis.

The use of NeuralWare Predict^® was a very effective method of implementing training rules for neural networks to identify the important attributes of numerical and nominal valued data.

The software and algorithms discussed in the paper can be used to visualize and mine microarray data.

The paper contributes to the discussion on the data visualization and data mining of microarray database for bioinformatics and emphasizes new applicability of modern heuristics and software.

This chapter proposes a nonparametric estimator of the risk neutral density (RND) based on cross-sectional European option prices. The authors recast the arbitrage-free equation for option pricing as a functional linear regression model where the regressor is a curve and the independent variable is a scalar corresponding to the option price. Then, the authors show that the RND can be viewed as the solution of an ill-posed integral equation. To estimate the RND, the authors use an iterative method called Landweber-Fridman (LF). Then, the authors establish the consistency and asymptotic normality of the estimated RND. These results can be used to construct a confidence interval around the curve. Finally, some Monte Carlo simulations and application to the S&P 500 options show that this method performs well compared to alternative methods.