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- Amiri, Saeid, 1976-
(författare)
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On the Application of the Bootstrap : Coefficient of Variation, Contingency Table, Information Theory and Ranked Set Sampling
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with the bootstrap method. Three decades after the seminal paper by Bradly Efron, still the horizons of this method need more exploration. The research presented herein has stepped into different fields of statistics where the bootstrap method can be utilized as a fundamental statistical tool in almost any application. The thesis considers various statistical problems, which is explained briefly below. Bootstrap method: A comparison of the parametric and the nonparametric bootstrap of variance is presented. The bootstrap of ranked set sampling is dealt with, as well as the wealth of theories and applications on the RSS bootstrap that exist nowadays. Moreover, the performance of RSS in resampling is explored. Furthermore, the application of the bootstrap method in the inference of contingency table test is studied. Coefficient of variation: This part shows the capacity of the bootstrap for inferring the coefficient of variation, a task which the asymptotic method does not perform very well. Information theory: There are few works on the study of information theory, especially on the inference of entropy. The papers included in this thesis try to achieve the inference of entropy using the bootstrap method.
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| 2. |
- Shahabsamani, Forough Shahab, et al.
(författare)
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Comparing Transfer Learning and Rollout for Policy Adaptation in a Changing Network Environment
- 2024
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Konferensbidrag (refereegranskat)abstract
- Dynamic resource allocation for network services is pivotal for achieving end-to-end management objectives. Previous research has demonstrated that Reinforcement Learning (RL) is a promising approach to resource allocation in networks, allowing to obtain near-optimal control policies for non-trivial system configurations. Current RL approaches however have the drawback that a change in the system or the management objective necessitates expensive retraining of the RL agent.To tackle this challenge, practical solutions including offline retraining, transfer learning, and model-based rollout have been proposed. In this work, we study these methods and present comparative results that shed light on their respective performance and benefits. Our study finds that rollout achieves faster adaptation than transfer learning, yet its effectiveness highly depends on the accuracy of the system model.
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