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Confidence intervals for discrete log-linear models when MLE does not exist

Author

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  • Wang, Nanwei
  • Massam, Hélène
  • Li, Qiong

Abstract

The aim of this paper is to provide a methodology and MATLAB programs to compute confidence intervals for the cell probability parameters in a high-dimensional discrete log-linear model when the maximum likelihood estimate of these parameters does not exist. To do so, we use the geometry of exponential families as well as recent methodology to identify the submodel for which the maximum likelihood estimate exists. We illustrate our results with both simulated and real world data.

Suggested Citation

  • Wang, Nanwei & Massam, Hélène & Li, Qiong, 2022. "Confidence intervals for discrete log-linear models when MLE does not exist," Statistics & Probability Letters, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:stapro:v:187:y:2022:i:c:s0167715222001018
    DOI: 10.1016/j.spl.2022.109532
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