990 resultados para Delzer, Marc
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Competitividad y valor compartido
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1998 Brock Badger men's baseball team photo. Front Row (L to R): Bill Gillen, Ryan Villers, Greg Arbour, Mark Cheeseman, Andrew Tinnish, Rick Bottomley, Matt Fletcher, Brad Namtzu, Darryl Presley, Dan Pino, Grant Giffen, Mike Caruso, Mark Reilly Back Row (L to R): Jeff Lounsbury (Head Coach), Jayar Green, Creston Rudolph, Ryan Fisher, Jamie Trull, Stefan Strecker, Andrew Robb, Jeremy Walker, Ryan Johns, Matt Stezycki, Steve Lester, Fabio Del Rio, Jarrod Haase, Jess Dixon, Rick Falconer (Pitching Coach) Absent: Marc Purdy, Ian Bala, Marc LePage (Asst. Coach), Waybe Briggs-Jude (Asst. Coach)
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UANL
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Communication présentée au 11e congrès biennal d'OLAC (OnLine Audiovisual Catalogers Conference), Montréal, le 1er octobre 2004.
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Les programmes de détention d’actions pour les employés : L’impact sur la profitabilité de la firme.
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It is well-known that non-cooperative and cooperative game theory may yield different solutions to games. These differences are particularly dramatic in the case of truels, or three-person duels, in which the players may fire sequentially or simultaneously, and the games may be one-round or n-round. Thus, it is never a Nash equilibrium for all players to hold their fire in any of these games, whereas in simultaneous one-round and n-round truels such cooperation, wherein everybody survives, is in both the a -core and ß -core. On the other hand, both cores may be empty, indicating a lack of stability, when the unique Nash equilibrium is one survivor. Conditions under which each approach seems most applicable are discussed. Although it might be desirable to subsume the two approaches within a unified framework, such unification seems unlikely since the two approaches are grounded in fundamentally different notions of stability.
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We consider the problem of testing whether the observations X1, ..., Xn of a time series are independent with unspecified (possibly nonidentical) distributions symmetric about a common known median. Various bounds on the distributions of serial correlation coefficients are proposed: exponential bounds, Eaton-type bounds, Chebyshev bounds and Berry-Esséen-Zolotarev bounds. The bounds are exact in finite samples, distribution-free and easy to compute. The performance of the bounds is evaluated and compared with traditional serial dependence tests in a simulation experiment. The procedures proposed are applied to U.S. data on interest rates (commercial paper rate).
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