@hall+martin:1988], and jackknife [@quenouille:1956; @efron:1982] can also apply to at least partially-specified models. In this way, they provide the means for improving estimation in involved modelling settings, where researchers have, historically, resorted to surrogate inference functions in an attempt to either limit the number of hard-to-justify modelling assumptions or because the full likelihood function is impractical or cumbersome to compute or construct; see, for example, @wedderburn:1974 for quasi likelihoods, @liang+zeger:1986 for generalized estimating equations, and @lindsay:1988 and @varin+reid+firth:2011 for composite likelihood methods.

Another criterion for classifying bias-reduction methods comes from @kosmidis:2014. Therein, bias-reduction methods are classified according to whether they operate in an explicit manner by estimating ${{}B}_G(\bar{{}\theta})$ from the data and subtracting that from $\hat{{}\theta}$, or in an implicit manner by replacing ${{}B}_G(\bar{{}\theta})$ with $\hat{{{}B}}_G(\tilde{{}\theta})$ in (\[bias\_equation\]) for some estimator $\hat{{{}B}}_G$ of the bias function, and solving the resulting implicit equation.

Bias-reduction methods can also be classified according to whether the necessary approximation of the bias term in (\[bias\_equation\]) is performed analytically or through simulation. The vanilla implementations of asymptotic bias correction and adjusted score functions approximate ${{}B}_G({{}\theta})$ with a function ${{}b}({{}\theta})$ such that ${{}B}_G({{}\theta}) = {{}b}({{}\theta}) + O(n^{-3/2})$, where $n$ is a measure of how the information about 