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Huson, A new model for scheduling packet radio networks, in Proceedings 15th Annual Joint Conference of the IEEE Computer and Communication Societies (IEEE INFOCOM 96) pp 1116 1124, 1996 40 I Stojmevovic and M Seddigh, Internal nodes based broadcasting algorithms in wireless networks, in Proceedings International Conference on Advances in Infrastructure for Electronic Business, Science and Education on the Internet, SSGRR, L Aquila, Italy, July 2000..

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Probability of error: Let (Xi , Yi ) p(x, y). De ne the events / E0 = {(X, Y) A(n) }, E1 = { x = X : f1 (x ) = f1 (X) and (x , Y) A(n) }, E2 = { y = Y : f2 (y ) = f2 (Y) and (X, y ) A(n) }, and E12 = { (x , y ) : x = X, y = Y, f1 (x ) = f1 (X), f2 (y ) = f2 (Y) and (x , y ) A(n) }. (15.170) (15.167) (15.168) (15.169)

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be used to analyze continuous response variables (ie, with an infinite number of thresholds) The Poisson model for count data, which Rasch described in 1960, is a special case of the ordinal Rasch model Although it assumes an upper limit for the number of response categories, this number differs from person to person (eg, the number of errors or attempts) Given independent events, this number is assumed to have a binomial distribution It was adopted by Safrit, Zhu, Costa, and Zhang (1992) to examine how difficulty varied in 18 different sit-ups tests for training abdominal muscles A total of 426 college students performed the 18 tests in an 8-week period The number of attempts within 1 min was recorded.

The simplest item was performance with hands on thighs and feet anchored, whereas the most difficult item was performance with hands clasped behind the neck and elbows pointed forward The third extension involves the number of latent traits Up to now, we have always considered one latent trait , that is, a person variable However, there are now a great number of multidimensional respective item component models Generally speaking, this means that the response to an item depends on several latent variables Although Rasch presented a multidimensional model in 1961, it contained the very restrictive assumption that the response probability for each response category depends on its own particular latent variable or person characteristic (see Rost, 2004, p 259) A more frequent special case, which is also applied in sport psychology, is Fischer s (1973, 1974, 1983) linearlogistic test model (LLTM).

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This model decomposes item difficulty additively into further components, for example, into various elements involved in processing a task The LLTM can also be used to analyze data sets with polytomous response categories, thereby applying the ordinal Rasch models (ie, partial credit model, rating scale model) Moreover, the LLTM and its variants are very suitable for analyzing change (Fischer, 1974, 1987; Gl ck & Spiel, 1997; Meiser, in press) In the sport psychology domain, we do not know any study that uses these fruitful approaches to analyze changes However, not only the item parameter but also the person parameter can vary in the LLTM This produces multidimensional models in which one person is assigned several person parameters The LLTM has been used frequently in the study of cognitive and motor operations.