• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.115-127
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• 2013

This paper provides an effective stock portfolio screening tool for socially responsible investment (SRI) based upon corporate social responsibility (CSR) and financial performance. The proposed approach utilizes nonparametric frontier models. Data envelopment analysis (DEA) has been used to build SRI portfolios in a few previous works; however, we show that free disposal hull (FDH), a similar model that does not assume the convexity of the technology, yields superior results when applied to a stock universe of 253 Korean companies. Over a four-year time span (from 2006 to 2009) the portfolios selected by the proposed method consistently outperform those selected by DEA as well as the benchmark.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.191-209
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• 2011

In a buyer-seller transaction process, 'value for money,' a measure of quality-price-ratio, is one of the most important criteria for buyers' purchasing decisions. The purpose of this paper is to suggest a method which helps online shoppers choose the best of several sellers offering homogeneous goods. We suggest FDH (free disposal hull) model, an applied model of data envelopment analysis (DEA), for online buyer-seller transactions and verify it with the data from an Internet comparison shopping site. For this purpose, we analyze consumer choice behaviors by examining how consumers respond to different sale conditions such as price, brand, or delivery time. Then, we implement a seller recommendation system to support buyers' purchasing decisions. We expect our FDH model to provide valuable information for rational buyers who want to pay the least price for high quality products/services and to be used in implementing automated evaluation processes in micro transactions. Moreover, we expect that our results can be utilized for sellers' benchmarking strategies which help sellers be more competitive by showing them how to attract buyers.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.265-280
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• 2001

Estimation of the edge of a distribution has many important applications. It is related to classification, cluster analysis, neural network, and statistical image recovering. The problem also arises in measuring production efficiency in economic systems. Three most promising nonparametric estimators in the existing literature are introduced. Their statistical properties are provided, some of which are new. Themes of future study are also discussed.