期刊名称：International Journal of Statistics and Probability
印刷版ISSN：1927-7032
电子版ISSN：1927-7040
出版年度：2015
卷号：4
期号：2
页码：132
DOI：10.5539/ijsp.v4n2p132
出版社：Canadian Center of Science and Education
摘要：Optimizing network usage is important to maximize the network performance. When the network usage grows rapidly, it is important to build an accurate predictive model. We present a new predictive algorithm which can analyze the network performance in various network conditions and traffic patterns. Our approach is based on the best predictive generalized linear mixed model (GLMM). The parameters of the best predictive GLMM are estimated by minimizing the mean squared prediction error (MSPE). To expedite the parameter learning with the big data collected through the network, our algorithm introduced regularization, LASSO, and an innovative bootstrap. The merits of our new approach validated through data and simulation are that (1) the highest prediction accuracy even under a model misspecification; and (2) the least computation time compared to the Estimation-oriented GLMM with Lasso and Stepwise Selection GLMM. A major computational advantage of our method is that, unlike some of the current approaches, our method does not require the EM (Expectation-Maximization algorithm) procedure.
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