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This paper develops a differential duopolistic game where goods are differentiated and prices are sticky, and the competitive and informative contents of advertising are explicitly considered, allowing advertising to have market size and business-stealing effects. Compared to previous literature, the novelty of this paper rests on the fact that this paper proposes the new assumption of the price adjustment...
This paper develops a differential duopolistic game model of optimal sticky prices and advertising in product differentiation market, and investigates the feedback Nash equilibrium of dynamic price and advertising interactions in a competitive setting. Specifically, we have made two contributions with respect to the available literature. First, we have proposed the new assumption of the price adjustment...
The number of variables used for credit scoring can be quite large, and selecting the most relevant variables becomes an important topic. In this paper, we use gradient learning method for variable selection in credit scoring. The original method in the literature does not work on credit datasets because of the large sample size. To conquer this, we modify the algorithm by resampling data and voting...
The sharing of data has been proven beneficial in data mining applications. However, privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. To resolve this challenging problem, data owners must design a solution that meets privacy requirements and guarantees valid data clustering results. To achieve this dual goal, we introduce a new method...
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and...
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