• Corpus ID: 237434340

Estimating nuisance parameters often reduces the variance (with consistent variance estimation)

  title={Estimating nuisance parameters often reduces the variance (with consistent variance estimation)},
  author={Judith J Lok},
  • J. Lok
  • Published 6 September 2021
  • Mathematics
In many applications, to estimate a parameter or quantity of interest ψ∗, a finite-dimensional nuisance parameter θ∗ is estimated first. For example, many estimators in causal inference depend on the estimated propensity score: the probability of (possibly time-dependent) treatment given the past. These nuisance parameters θ∗ are often estimated in a first step, which can affect the variance of the estimator for ψ∗. θ∗ is often estimated by maximum (partial) likelihood. Inverse Probability… 


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