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Robust mean and covariance structure analysis through iteratively reweighted least squares Psychometrika, 65, 43 58 Yuan, K-H, & Bentler, P M (2000b) Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data Sociological methodology (pp 165 200) Washington, DC: American Sociological Association Yuan, K-H, & Bentler, P M (in press) Effect of outliers on estimators and tests in covariance structure analysis British Journal of Mathematical and Statistical Psychology Yuan, K-H, Chan, W, & Bentler, P M (2000) Robust transformation with applications to structural equation modeling British Journal of Mathematical and Statistical Psychology, 53, 31 50 Yuan, K-H, & Jennrich, R I (2000) Estimating equations with nuisance parameters: Theory and applications Annals of the Institute of Statistical Mathematics, 52, 343 350 Yung, Y-F, & Bentler, P M (1996) Bootstrapping techniques in analysis of mean and covariance structures.
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In G A Marcoulides & R E Schumacker (Eds), Advanced structural equation modeling techniques (pp 195 226) Hillsdale, NJ: Erlbaum Zhu, H-T, & Lee, S-Y (1999) Statistical analysis of nonlinear factor analysis models British Journal of Mathematical and Statistical Psychology, 52, 225 242..
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Analysis of quasi-experimental time series designs Multivariate Behavioral Research, 12, 111 131 Tobler, N (1994) Meta-analytic issues for prevention intervention research In L M Collins & L A Seitz, (Eds), Advances in data analysis for prevention Intervention Research (NIDA Research Monograph 142, pp 342 403) Rockville, MD: National Institute on Drug Abuse Tomkins, S S (1966) Psychological model for smoking behavior American Journal of Public Health, 68, 250 257 Tomkins, S S (1968) A modi ed model of smoking behavior In E F Borgatta & R R Evans (Eds), Smoking, health and behavior (pp 165 186) Chicago: Aldine Tsay, R S (1984) Regression models with time series errors Journal of the American Statistical Association, 79, 118 124 Tsay, R S, & Tiao, G C (1984) Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARIMA models.
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1. N. Alon and M. Tarsi Colorings and orientations of graphs, Combinatorica, 12(2): 125 134, 1992. 2. J. Bondy and U. Murty, Graph Theory with Applications, London: Macmillan, 1976. 3. A. Borodin and R. El-Yaniv, Online Computation and Competitive Analysis, Cambridge: Cambridge University Press, 1998. 4. I. Caragiannis, C. Kaklamanis, and E. Papaionnou, Efficient online communication in cellular networks, in Symposium on Parallel Algorithms and Architecture, Bar Harbor, ME: ACM Press, 2000. 5. D. C. Cox and D. O. Reudink, Dynamic channel assignment in two dimension large-scale mobile radio systems, Bell Sys. Tech. J., 51: 1611 28, 1972. 6. P. Cresenzi, G. Gambosi, and P. Penna, Online algorithms for the channel assignment problem in cellular networks, in Proceedings of Dial M for Mobility, Boston: ACM Press, 2000. 7. S. Engel and M. M. Peritsky, Statistically-optimum dynamic server assignment in systems with interfering servers, IEEE Transactions on Vehicular Technology, 22: 203 209, 1973. 8. P. Erdos, A. L. Rubin, and H. Taylor, Choosability in graphs, in Proceedings of the West Coast Conference on Combinatorics, Graph Theory, and Computing, pp. 125 157, 1979. 9. T. Feder and S. M. Shende, Online channel allocation in FDMA networks with reuse constraints, Inform. Process. Lett., 67(6): 295 302, 1998. 10. A. Gamst, Some lower bounds for a class of frequency assignment problems, IEEE Trans. Veh. Technol., 35(1): 8 14, 1986. 11. N. Garg, M. Papatriantafilou, and T. Tsigas, Distributed list coloring: how to dynamically allocate frequencies to mobile base stations, in Symposium on Parallel and Distributed Processing, pp. 18 25, 1996. 12. A. Graf, M. Stumpf, and G. Weibenfels, On coloring unit disk graphs, Algorithmica, 20: 277 293, 1998.
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Journal of American Statistical Association, 79, 84 90 van Buuren, S (1997) Fitting ARMA time series models by structural equation models Psychometrika, 62, 215 236 Velicer, W F, & Colby, S M (1997) Time series analysis for prevention and treatment research In K J Bryant, M Windle, & S G West (Eds), The science of prevention: Methodological advances from alcohol and substance abuse research (pp 211 249) Washington, DC: American Psychological Association Velicer, W F, & Colby, S M (2001) A comparison of missing data procedures in Time Series Analysis Manuscript under review, University of Rhode Island Velicer, W F, Fraser, C, McDonald, R P, & Harrop, J W (1986) GENTS [Computer program] Kingston: University of Rhode Island Velicer, W F, & Harrop, J W (1983) The reliability and accuracy of time series model identi cation Evaluation Review, 7, 551 560..
In addition to storing power on a wireless node, in certain circumstances power can be distributed to the node from a nearby energy-rich source. It is dif cult to characterize the effectiveness of power-distribution methods by the same metrics (power or energy density), because in most cases the power received at the node is more a function of how much power is emitted rather than the size of the power receiver at the node. Nevertheless, an effort is made to characterize the effectiveness of a few power distribution methods as they apply to wireless sensor networks. 3.4.1 Electromagnetic (RF) Power Distribution
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