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On latent process models in multi-dimensional space

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  • Shang, Zuofeng

Abstract

Latent process models have been widely applied to time series and spatial data which involve complex correlation structures. However, the existing approaches assume a known distributional property of the observations given the latent process. Furthermore, there seems to be no literature treating the asymptotic properties of the latent process model in general multi-dimensional space (with dimension bigger than 2). In this paper, we propose to estimate the unknown model parameters of the latent process model in multi-dimensional space by an M-estimation approach, and derive the asymptotic normality, together with the explicit limiting variance matrix, for the estimates. The proposed method is of a distribution-free feature. Applications in three concrete situations are demonstrated.

Suggested Citation

  • Shang, Zuofeng, 2012. "On latent process models in multi-dimensional space," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1259-1266.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:7:p:1259-1266
    DOI: 10.1016/j.spl.2012.03.022
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    References listed on IDEAS

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