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M-Estimation for dependent random variables

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  • Furrer, Reinhard

Abstract

This paper discusses the consistency in the strong sense and essential uniqueness of M-estimation for dependent random variables. The hypotheses are based on the function defining implicitly the M-estimation as well as on its first derivative and its Hessian matrix. No explicit hypotheses on the random variables are necessary for consistency and uniqueness, thus the framework holds for any stochastic process.

Suggested Citation

  • Furrer, Reinhard, 2002. "M-Estimation for dependent random variables," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 337-341, May.
  • Handle: RePEc:eee:stapro:v:57:y:2002:i:4:p:337-341
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    References listed on IDEAS

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    1. Mukherjee, Kanchan, 1994. "Minimum distance estimation in linear models with long-range dependent errors," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 347-355, December.
    2. Heijmans, Risto D. H. & Magnus, Jan R., 1986. "Consistent maximum-likelihood estimation with dependent observations : The general (non-normal) case and the normal case," Journal of Econometrics, Elsevier, vol. 32(2), pages 253-285, July.
    3. Heijmans, R.D.H. & Magnus, J.R., 1986. "Consistent maximum-likelihood estimation with dependent observations : the general (non-normal) case and the normal case," Other publications TiSEM 9b460ea9-57c5-4d5b-934f-c, Tilburg University, School of Economics and Management.
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