### Abstract

Language | English |
---|---|

Pages | 35 - 48 |

Number of pages | 14 |

Journal | Statistical Methodology |

Volume | 21 |

Early online date | 4 Mar 2014 |

DOIs | |

Status | Published - 1 Nov 2014 |

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### Keywords

- stat.ME

### Cite this

*Statistical Methodology*,

*21*, 35 - 48. https://doi.org/10.1016/j.stamet.2014.02.004

**Bayesian Inference in Nonparametric Dynamic State-Space Models.** / Ghosh, Anurag; Mukhopadhyay, Soumalya; Roy, Sandipan; Bhattacharya, Sourabh.

Research output: Contribution to journal › Article

*Statistical Methodology*, vol. 21, pp. 35 - 48. https://doi.org/10.1016/j.stamet.2014.02.004

}

TY - JOUR

T1 - Bayesian Inference in Nonparametric Dynamic State-Space Models

AU - Ghosh, Anurag

AU - Mukhopadhyay, Soumalya

AU - Roy, Sandipan

AU - Bhattacharya, Sourabh

N1 - This version contains much greater clarification of the look-up table idea and a theorem regarding this is also proven and included in the supplement. Will appear in Statistical Methodology

PY - 2014/11/1

Y1 - 2014/11/1

N2 - We introduce state-space models where the functionals of the observational and the evolutionary equations are unknown, and treated as random functions evolving with time. Thus, our model is nonparametric and generalizes the traditional parametric state-space models. This random function approach also frees us from the restrictive assumption that the functional forms, although time-dependent, are of fixed forms. The traditional approach of assuming known, parametric functional forms is questionable, particularly in state-space models, since the validation of the assumptions require data on both the observed time series and the latent states; however, data on the latter are not available in state-space models. We specify Gaussian processes as priors of the random functions and exploit the "look-up table approach" of \ctn{Bhattacharya07} to efficiently handle the dynamic structure of the model. We consider both univariate and multivariate situations, using the Markov chain Monte Carlo (MCMC) approach for studying the posterior distributions of interest. In the case of challenging multivariate situations we demonstrate that the newly developed Transformation-based MCMC (TMCMC) of \ctn{Dutta11} provides interesting and efficient alternatives to the usual proposal distributions. We illustrate our methods with a challenging multivariate simulated data set, where the true observational and the evolutionary equations are highly non-linear, and treated as unknown. The results we obtain are quite encouraging. Moreover, using our Gaussian process approach we analysed a real data set, which has also been analysed by \ctn{Shumway82} and \ctn{Carlin92} using the linearity assumption. Our analyses show that towards the end of the time series, the linearity assumption of the previous authors breaks down.

AB - We introduce state-space models where the functionals of the observational and the evolutionary equations are unknown, and treated as random functions evolving with time. Thus, our model is nonparametric and generalizes the traditional parametric state-space models. This random function approach also frees us from the restrictive assumption that the functional forms, although time-dependent, are of fixed forms. The traditional approach of assuming known, parametric functional forms is questionable, particularly in state-space models, since the validation of the assumptions require data on both the observed time series and the latent states; however, data on the latter are not available in state-space models. We specify Gaussian processes as priors of the random functions and exploit the "look-up table approach" of \ctn{Bhattacharya07} to efficiently handle the dynamic structure of the model. We consider both univariate and multivariate situations, using the Markov chain Monte Carlo (MCMC) approach for studying the posterior distributions of interest. In the case of challenging multivariate situations we demonstrate that the newly developed Transformation-based MCMC (TMCMC) of \ctn{Dutta11} provides interesting and efficient alternatives to the usual proposal distributions. We illustrate our methods with a challenging multivariate simulated data set, where the true observational and the evolutionary equations are highly non-linear, and treated as unknown. The results we obtain are quite encouraging. Moreover, using our Gaussian process approach we analysed a real data set, which has also been analysed by \ctn{Shumway82} and \ctn{Carlin92} using the linearity assumption. Our analyses show that towards the end of the time series, the linearity assumption of the previous authors breaks down.

KW - stat.ME

UR - https://www.sciencedirect.com/science/article/pii/S1572312714000197

U2 - 10.1016/j.stamet.2014.02.004

DO - 10.1016/j.stamet.2014.02.004

M3 - Article

VL - 21

SP - 35

EP - 48

JO - Statistical Methodology

T2 - Statistical Methodology

JF - Statistical Methodology

SN - 1572-3127

ER -