Inference with non-homogeneous lognormal diffusion processes conditioned on nearest neighbor

HIGHLIGHTS

• What: The authors develop a simulation and obtain a dataset that is closer to reality where there is no regularity in the number or timing of observations to extend the traditional inference method. For such data the authors propose using characteristic functions the past or the future depending the closest point at which the authors aim to perform the imputation. The authors propose a methodology to use non-homogeneous lognormal diffusion processes for modeling and making inferences. Specifically, the authors propose inference using information not only from the past but also from the future, particularly using the . . .
  

   

  

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