This paper extends the projected score methods of Small & McLeish (1989). It is shown that the conditional score function may be approximated, with arbitrarily small stochastic error, in terms of a natural basis for the space of centred likelihood ratios. The utility of using this basis is established by identifying a [/-statistic representation theorem and a class of expectation identities for the basis elements, making higher order asymptotics more tractable. The results are applied to a canonical exponential family model, where it is shown that the projected scores with estimated nuisance parameters can provide an accurate approximation to the conditional score function
Recently developed small-sample asymptotics provide nearly exact inference for parametric statistica...
We establish strong consistency and asymptotic normality of the maximum likelihood estimator for sto...
Under suitable regularity conditions, an appropriate improved score statistic was derived recently b...
[[abstract]]The conditional score approach is proposed to the analysis of errors-in-variable current...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
For testing canonical parameters in a continuous exponential family, P-values based on higher order ...
We discuss the statistical properties of a recently introduced unbiased stochastic approximation to ...
The paper considers a rectangular array asymptotic embedding for multistratum data sets, in which bo...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
For submodels of an exponential family, we consider likelihood ratio tests for hypotheses that rende...
Recently developed small-sample asymptotics provide nearly exact inference for parametric statistica...
This paper describes an algorithm for maximising a conditional likelihood function when the correspo...
We propose and motivate an expanded version of the logarithmic score for forecasting distributions,...
A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When d...
Recently developed small-sample asymptotics provide nearly exact inference for parametric statistica...
We establish strong consistency and asymptotic normality of the maximum likelihood estimator for sto...
Under suitable regularity conditions, an appropriate improved score statistic was derived recently b...
[[abstract]]The conditional score approach is proposed to the analysis of errors-in-variable current...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
This paper presents a set of REDUCE procedures making a number of existing higher-order asymptotic r...
For testing canonical parameters in a continuous exponential family, P-values based on higher order ...
We discuss the statistical properties of a recently introduced unbiased stochastic approximation to ...
The paper considers a rectangular array asymptotic embedding for multistratum data sets, in which bo...
This paper presents a set of REDUCE procedures that make a number of existing higher-order asymptoti...
For submodels of an exponential family, we consider likelihood ratio tests for hypotheses that rende...
Recently developed small-sample asymptotics provide nearly exact inference for parametric statistica...
This paper describes an algorithm for maximising a conditional likelihood function when the correspo...
We propose and motivate an expanded version of the logarithmic score for forecasting distributions,...
A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When d...
Recently developed small-sample asymptotics provide nearly exact inference for parametric statistica...
We establish strong consistency and asymptotic normality of the maximum likelihood estimator for sto...
Under suitable regularity conditions, an appropriate improved score statistic was derived recently b...