Finite sample nonparametric inference and large sample efficiency

Finite sample inference for extreme value distributions

We consider the problem of small sample inference for the generalised extreme value distribution. In particular, we show the existence of approximate and exact ancillary statistics for this distribution and that small sample likelihood based inference is greatly improved by conditioning on these statistics. Ignoring the ancillary statistics in inference can have severe consequences in some stan...

Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics

Given a sample of size n from a population of individual belonging to different species with unknown proportions, a popular problem of practical interest consists in making inference on the probability Dn(l) that the (n + 1)-th draw coincides with a species with frequency l in the sample, for any l = 0, 1, . . . , n. This paper contributes to the methodology of Bayesian nonparametric inference ...

Large-Sample Inference on Spatial Dependence

We consider cross-sectional data that exhibit no spatial correlation, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of …nancial econometrics, entailing a form of spatial autoregression, can explain such behaviour. The parameters are estimated by pseudo Gaussian maximum likelihood based on log-transformed squares, and consisten...

Twenty Nonparametric Statistics And Their Large Sample Approximations

Large Sample Sieve Estimation of Semi-nonparametric Models By

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