950 resultados para automobile racing


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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Thormanby pseudonym for Willmott Willmott-Dixon.

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This study presents the first analysis of the impact of NASCAR sponsorship announcements on the stock prices of sponsoring firms. The primary finding of the study-that NASCAR sponsorship announcements were accompanied by the largest increases in shareholder wealth ever recorded in the marketing literature in response to a voluntary marketing program-represents a striking and unambiguous stock market endorsement of the sponsorships. Indeed, the 24 sponsors analyzed in this study experienced mean increases in shareholder wealth of over $300 million dollars, net of all of the costs associated with the sponsorships. A multiple regression analysis of firm-specific stock price changes and select corporate and sponsorship attributes indicates that NASCAR sponsorships with more successful racing teams, corporate (as opposed to product or divisional) sponsorships, and sponsorships with direct ties to the consumer automotive industry are all positively correlated with perceived sponsorship success, while corporate cash flow per share (a well-known proxy for agency conflicts within the firm) is negatively related with shareholder approval.

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In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

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Racing algorithms have recently been proposed as a general-purpose method for performing model selection in machine teaming algorithms. In this paper, we present an empirical study of the Hoeffding racing algorithm for selecting the k parameter in a simple k-nearest neighbor classifier. Fifteen widely-used classification datasets from UCI are used and experiments conducted across different confidence levels for racing. The results reveal a significant amount of sensitivity of the k-nn classifier to its model parameter value. The Hoeffding racing algorithm also varies widely in its performance, in terms of the computational savings gained over an exhaustive evaluation. While in some cases the savings gained are quite small, the racing algorithm proved to be highly robust to the possibility of erroneously eliminating the optimal models. All results were strongly dependent on the datasets used.

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