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Psychosocial factors related to eating disorders among high school and college female cheerleaders Sport Psychologist, 10, 195 206 Reinking, M, & Alexander, L (2005) Prevalence of disorderedeating behaviors in undergraduate female collegiate athletes and nonathletes Journal of Athletic Training, 40, 47 51 Rhea, D J (1999) Eating disorder behaviors of ethnically diverse urban female adolescent athletes and non-athletes Journal of Adolescence, 22, 379 388 Rodin, J, Silberstein, L, & Striegel-Moore, R (1985) Women and weight: A normative discontent In T B Sonderegger (Ed), Nebraska Symposium on Motivation: Psychology and gender (pp 267 307) Lincoln: University of Nebraska Press Rogers, R, & Petrie, T (2001) Psychological correlates of anorexic and bulimic symptomatology Journal of Counseling and Development, 79, 178 187 Rome, E, & Ammerman, S (2003) Medical complications in eating disorders: An update Journal of Adolescent Health, 33, 418 426 Rosen, L W.

, & Hough, D O (1988) Pathogenic weight-control behaviors in female college gymnasts Physician and Sportsmedicine, 16(9), 141 146 Rosen, L W, McKeag, D B, Hough, D O, & Curley, V (1986) Pathogenic weight control behavior in female athletes Physician and Sportsmedicine, 14(1), 79 95 Rucinski, A (1989) Relationship of body image and dietary intake of competitive ice skaters Journal of American Dietetic Association, 89, 98 99 Ryan, R (1992) Management of eating problems in athletic settings In K Brownell, J Rodin, & J Wilmore (Eds), Eating, body weight and performance in athletes: Disorders of modern society (pp 344 362) Philadelphia: Lea & Febiger Ryujin, D H, Breaux, C, & Marks, A D (1999) Symptoms of eating disorders among female distance runners: Can the inconsistencies be unraveled Women and Health, 30, 71 83..

follows from Lemma 17.10.1 follows from the fact that the conditioning decreases entropy follows from the fact that Z is a function of X and Y follows since Xn + Yn is Gaussian conditioned on X1 , X2 , . . . , Xn 1 , Y1 , Y2 , . . . , Yn 1 , and hence we can express its entropy in terms of its variance (e) follows from the independence of Xn and Yn conditioned on the past X1 , X2 , . . . , Xn 1 , Y1 , Y2 , . . . , Yn 1 (f) follows from the fact that for a set of jointly Gaussian random variables, the conditional variance is constant, independent of the conditioning variables (Lemma 17.10.1) Setting A = S and B = T , we obtain |Tn | |Sn | | Sn + Tn | + |Sn 1 | |Tn 1 | | Sn 1 + Tn 1 | (17.156)

Purisch, A D, 229 Puryear Keita, G, 281, 282 Putnam, A J, 380 Pyszczynski, T, 13 Queen, L, 478 Quilitch, H R, 90, 91 Rabasca, L, 452, 453 Rabin, A S, 305 Rachman, S, 100, 103, 303 Rafanelli, C, 307 Rafferty, J E, 248 Ragnar, D S, 434 Raichle, M E, 152 Raikkonen, K, 166 Raimy, V C, 484 Range, L M, 306 Rapee, R M, 333 Ratcliff, K S, 193, 194, 195 Rauch, S L, 160 Raven, J C, 226 Raw, S D, 109, 457 Raymer, R, 337, 338 Rayner, R, 47 Redmond, W H, 326 Reed, D, 330 Reed, G M, 233, 235 Reed, T E, 231 Regier, D A, 154 Rehm, L P.

total number of connections and the number of available subchannels, respectively 2153 Iterative Subchannel Allocation The iterative subchannel allocation is based on a water- lling algorithm First, the algorithm tries to allocate subchannels among the connections such that the transmission rate requirements are met In this case, the number of subchannels allocated to relay and local connections must be limited to the corresponding threshold (ie, Tr and Tl = N Tr, respectively) If the requirements for all the connections are satis ed, the algorithm tries to allocate the subchannels among the different connections so that the total transmission rate at the BS is maximized This procedure is shown in Algorithm 2151 Note that the complexity of the iterative subchannel allocation algorithm is O(N C), which is much smaller than that of the optimal algorithm 215.