Julie McIntyre

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Data swapping, a term introduced in 1978 by Dalenius and Reiss for a new method of statistical disclosure protection in confidential data bases, has taken on new meanings and been linked to new statistical methodologies over the intervening twenty-five years. This paper revis-its the original (1982) published version of the the Dalenius-Reiss data swapping(More)
We present a deconvolution estimator for the density function of a random variable from a set of independent replicate measurements. We assume that measurements are made with normally distributed errors having unknown and possibly heterogeneous variances. The estimator generalizes the deconvoluting kernel density estimator of Stefanski and Carroll (1990),(More)
Density estimates based on point processes are often restrained to regions with irregular boundaries or holes. We propose a density estimator, the lattice-based density estimator, which produces reasonable density estimates under these circumstances. The estimation process starts with overlaying the region with nodes, linking these together in a lattice and(More)
The twentieth century has seen major transformations in Australian society. From the aftermath of the Great War, the course follows the traumas of Depression and World War II, into periods of less dramatic but still profound change: postwar boom through the Menzies years, threats posed by the Cold War, the Bomb and the discovery of the teenager, the impact(More)
We present a semi-parametric deconvolution estimator for the density function of a random variable biX that is measured with error, a common challenge in many epidemiological studies. Traditional deconvolution estimators rely only on assumptions about the distribution of X and the error in its measurement, and ignore information available in auxiliary(More)
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