Improvements on Parameter Estimation based on Particle Approximations of the Gradient and Information Matrix in State Space Models

Autores

  • Uriel M. Silva
  • Rodolfo Santos Nunes Rodrigues
  • Felipe Carvalho Álvares da Silva
  • Luiz Henrique Duczmal
  • Denise Burgarelli Duczmal

Resumo

Let (Xt , Yt )t≥1 be a homogeneous discrete-time bivariate stochastic process where (Xt )t≥1 is a Markov chain and (Yt |Xt )t≥1 is a conditionally independent sequence such that each Yt is determined almost surely by Xt . If we also assume that only (Yt )t≥1 is available for inference, i.e. that the Markov chain (Xt )t≥1 is unobservable, then (Xt , Yt )t≥1 is usually called a state space or hidden Markov model (HMM). Usually, the law of (Xt , Yt )t≥1 is also taken to be indexed by a d-dimensional parameter θ taking values in Θ.

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Publicado

2018-12-19

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