Search results for: Discriminant

Authors: Kyoung-jae Kim

Abstract:

Recommender systems are usually regarded as an important marketing tool in the e-commerce. They use important information about users to facilitate accurate recommendation. The information includes user context such as location, time and interest for personalization of mobile users. We can easily collect information about location and time because mobile devices communicate with the base station of the service provider. However, information about user interest can-t be easily collected because user interest can not be captured automatically without user-s approval process. User interest usually represented as a need. In this study, we classify needs into two types according to prior research. This study investigates the usefulness of data mining techniques for classifying user need type for recommendation systems. We employ several data mining techniques including artificial neural networks, decision trees, case-based reasoning, and multivariate discriminant analysis. Experimental results show that CHAID algorithm outperforms other models for classifying user need type. This study performs McNemar test to examine the statistical significance of the differences of classification results. The results of McNemar test also show that CHAID performs better than the other models with statistical significance.

Keywords: Customer need type, Data mining techniques, Recommender system, Personalization, Mobile user.

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Authors: Andrea Bellucci, Martina Tofi

Abstract:

The aim of paper is to analyze business models of bancassurance in Italy for life business. The life insurance business is very developed in the Italian market and banks branches have 80% of the market share. Given its maturity, the life insurance market needs to consolidate its organizational form to allow for the development of non-life business, which nowadays collects few premiums but represents a great opportunity to enlarge the market share of bancassurance using its strength in the distribution channel while the market share of independent agents is decreasing. Starting with the main business model of bancassurance for life business, this paper will analyze the performances of life companies in the Italian market by balance sheet indicators and by main discriminant variables of business models. The study will observe trends from 2013 to 2015 for the Italian market by exploiting a database managed by Associazione Nazionale delle Imprese di Assicurazione (ANIA). The applied approach is based on a bottom-up analysis starting with variables and indicators to define business models’ classification. The statistical classification algorithm proposed by Ward is employed to design business models’ profiles. Results from the analysis will be a representation of the main business models built by their profile related to indicators. In that way, an unsupervised analysis is developed that has the limit of its judgmental dimension based on research opinion, but it is possible to obtain a design of effective business models.

Keywords: Balance sheet indicators, Bancassurance, business models, ward algorithm.

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Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the creditscoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: Credit-scoring Models, Multidimensional Subordinated Lévy Model, Probability of Default.

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Authors: F. Rahnamay Roodposhti, M. Falah Shams, H. Kordlouie

Abstract:

The aim of the article is extending and developing econometrics and network structure based methods which are able to distinguish price manipulation in Tehran stock exchange. The principal goal of the present study is to offer model for approximating price manipulation in Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of manipulated and non manipulated companies. In the next stage by investigating cumulative return process and volume of trades in manipulated companies, the date of starting price manipulation was specified and in this way the logit model, artificial neural network, multiple discriminant analysis and by using information related to size of company, clarity of information, ratio of P/E and liquidity of stock one year prior price manipulation; a model for forecasting price manipulation of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting models were studied by using data of test set. Whereas the power of forecasting logit model for test set was 92.1%, for artificial neural network was 94.1% and multi audit analysis model was 90.2%; therefore all of the 3 aforesaid models has high power to forecast price manipulation and there is no considerable difference among forecasting power of these 3 models.

Keywords: Price Manipulation, Liquidity, Size of Company, Floating Stock, Information Clarity

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Authors: A. Abdullah, N. Faghih Mirzaei, S. Hany Haron

Abstract:

Neighbourhood environment walkability on reported physical activity (PA) levels of students of Universiti Sains Malaysia (USM) in Malaysia. Compared with previous generations, today’s young people spend less time playing outdoors and have lower participation rates in PA. Research suggests that negative perceptions of neighbourhood walkability may be a potential barrier to adolescents’ PA. The sample consisted of 200 USM students (to 24 years old) who live outside of the main campus and engage in PA in sport halls and sport fields of USM. The data were analysed using the t-test, binary logistic regression, and discriminant analysis techniques. The present study found that youth PA was affected by neighbourhood environment walkability factors, including neighbourhood infrastructures, neighbourhood safety (crime), and recreation facilities, as well as street characteristics and neighbourhood design variables such as facades of sidewalks, roadside trees, green spaces, and aesthetics. The finding also illustrated that active students were influenced by street connectivity, neighbourhood infrastructures, recreation facilities, facades of sidewalks, and aesthetics, whereas students in the less active group were affected by access to destinations, neighbourhood safety (crime), and roadside trees and green spaces for their PAs. These results report which factors of built environments have more effect on youth PA and they message to the public to create more awareness about the benefits of PA on youth health.

Keywords: Fear of crime, neighbourhood built environment, physical activities, street characteristics design.

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Authors: Satyanadh Gundimada, Vijayan K Asari

Abstract:

A novel feature selection strategy to improve the recognition accuracy on the faces that are affected due to nonuniform illumination, partial occlusions and varying expressions is proposed in this paper. This technique is applicable especially in scenarios where the possibility of obtaining a reliable intra-class probability distribution is minimal due to fewer numbers of training samples. Phase congruency features in an image are defined as the points where the Fourier components of that image are maximally inphase. These features are invariant to brightness and contrast of the image under consideration. This property allows to achieve the goal of lighting invariant face recognition. Phase congruency maps of the training samples are generated and a novel modular feature selection strategy is implemented. Smaller sub regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are arranged in the order of increasing distance between the sub regions involved in merging. The assumption behind the proposed implementation of the region merging and arrangement strategy is that, local dependencies among the pixels are more important than global dependencies. The obtained feature sets are then arranged in the decreasing order of discriminating capability using a criterion function, which is the ratio of the between class variance to the within class variance of the sample set, in the PCA domain. The results indicate high improvement in the classification performance compared to baseline algorithms.

Keywords: Discriminant analysis, intra-class probability distribution, principal component analysis, phase congruency.

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Authors: Chau-Kuang Chen, Juanita Buford, Colette Davis, Raisha Allen, John Hughes, Jr., James Tyus, Dexter Samuels

Abstract:

During the post-Civil War era, the city of Nashville, Tennessee, had the highest mortality rate in the United States. The elevated death and disease rates among former slaves were attributable to lack of quality healthcare. To address the paucity of healthcare services, Meharry Medical College, an institution with the mission of educating minority professionals and serving the underserved population, was established in 1876. Purpose: The social ecological framework and partial least squares (PLS) path modeling were used to quantify the impact of socioeconomic status and adverse health outcome on primary care professionals serving the disadvantaged community. Thus, the study results could demonstrate the accomplishment of the College’s mission of training primary care professionals to serve in underserved areas. Methods: Various statistical methods were used to analyze alumni data from 1975 – 2013. K-means cluster analysis was utilized to identify individual medical and dental graduates in the cluster groups of the practice communities (Disadvantaged or Non-disadvantaged Communities). Discriminant analysis was implemented to verify the classification accuracy of cluster analysis. The independent t-test was performed to detect the significant mean differences of respective clustering and criterion variables. Chi-square test was used to test if the proportions of primary care and non-primary care specialists are consistent with those of medical and dental graduates practicing in the designated community clusters. Finally, the PLS path model was constructed to explore the construct validity of analytic model by providing the magnitude effects of socioeconomic status and adverse health outcome on primary care professionals serving the disadvantaged community. Results: Approximately 83% (3,192/3,864) of Meharry Medical College’s medical and dental graduates from 1975 to 2013 were practicing in disadvantaged communities. Independent t-test confirmed the content validity of the cluster analysis model. Also, the PLS path modeling demonstrated that alumni served as primary care professionals in communities with significantly lower socioeconomic status and higher adverse health outcome (p < .001). The PLS path modeling exhibited the meaningful interrelation between primary care professionals practicing communities and surrounding environments (socioeconomic statues and adverse health outcome), which yielded model reliability, validity, and applicability. Conclusion: This study applied social ecological theory and analytic modeling approaches to assess the attainment of Meharry Medical College’s mission of training primary care professionals to serve in underserved areas, particularly in communities with low socioeconomic status and high rates of adverse health outcomes. In summary, the majority of medical and dental graduates from Meharry Medical College provided primary care services to disadvantaged communities with low socioeconomic status and high adverse health outcome, which demonstrated that Meharry Medical College has fulfilled its mission. The high reliability, validity, and applicability of this model imply that it could be replicated for comparable universities and colleges elsewhere.

Keywords: Disadvantaged Community, K-means Cluster Analysis, PLS Path Modeling, Primary care.

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