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Simple relaxed conditional likelihood

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  • John J. Hanfelt
  • Lijia Wang

Abstract

When the data are sparse but not exceedingly so, we face a trade-off between bias and precision that makes the usual choice between conducting either a fully unconditional inference or a fully conditional inference unduly restrictive. We propose a method to relax the conditional inference that relies upon commonly available computer outputs. In the rectangular array asymptotic setting, the relaxed conditional maximum likelihood estimator has smaller bias than the unconditional estimator and smaller mean square error than the conditional estimator.

Suggested Citation

  • John J. Hanfelt & Lijia Wang, 2014. "Simple relaxed conditional likelihood," Biometrika, Biometrika Trust, vol. 101(3), pages 726-732.
    • Handle: RePEc:oup:biomet:v:101:y:2014:i:3:p:726-732.
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    File URL: http://hdl.handle.net/10.1093/biomet/asu028
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    Cited by:

    1. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.

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