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On Consistency of Estimators Based on Random Set Vector Observations

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Book cover Causal Inference in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 622))

Abstract

In this paper, the characterization of the joint distribution of random set vector by the belief function is investigated. A routine of calculating the bivariate coarsening at random model of finite random sets is obtained. In the context of reliable computations with imprecise data, we show that the maximum likelihood estimators of parameters in CAR model are consistent. Several examples are given to illustrate our results.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA

    Zheng Wei

  2. Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, USA

    Zheng Wei & Tonghui Wang

  3. School of Statistics, Southwestern University of Finance and Economy, Qingyang, China

    Baokun Li

Authors
  1. Zheng Wei
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  2. Tonghui Wang
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  3. Baokun Li
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Corresponding author

Correspondence to Tonghui Wang .

Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

    Songsak Sriboonchitta

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Wei, Z., Wang, T., Li, B. (2016). On Consistency of Estimators Based on Random Set Vector Observations. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics. Studies in Computational Intelligence, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-27284-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-27284-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27283-2

  • Online ISBN: 978-3-319-27284-9

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