Sayın, Muhammed O.Vanlı, Nuri DenizcanKozat, Süleyman Serdar2016-02-082016-02-0820141520-6149http://hdl.handle.net/11693/27374Date of Conference: 4-9 May 2014Conference Name: 39th International Conference on Acoustics, Speech and Signal Processing, IEEE 2014We provide a logarithmic upper-bound on the regret function of the diffusion implementation for the distributed estimation. For certain learning rates, the bound shows guaranteed performance convergence of the distributed least mean square (DLMS) algorithms to the performance of the best estimation generated with hindsight of spatial and temporal data. We use a new cost definition for distributed estimation based on the widely-used statistical performance measures and the corresponding global regret function. Then, for certain learning rates, we provide an upper-bound on the global regret function without any statistical assumptions.EnglishEstimationSignal processingCost definitionDistributedDistributed estimationGuaranteed performanceLearning ratesLeast mean squaresRegretStatistical performance measuresDiffusionConference Paper10.1109/ICASSP.2014.6855217