A neural network classification based noise identification method is presented by isolating some representative noise samples, and extracting their statistical features for noise type identification. The isolation of representative noise samples is achieved using prevalent used image filters whereas noise identification is performed using statistical moments features based classification system...

A class of models is proposed for longitudinal network data. These models are along the lines of methodological individualism: actors use heuristics to try to achieve their individual goals, subject to constraints. The current network structure is among these constraints. The models are continuous time Markov chain models that can be implemented as simulation models. They incorporate random cha...

We introduce the Method of Simulated Moments (MSM) for estimating unknown parameters in dynamic models. The MSM is useful when there are empirical data related to the behavior of different entities and error terms do not follow any well-established distribution. Statistical moments such as mean and variance of empirical data can be matched against the moments of model-generated data in order to...