Multiple-component radiation-measurement error models.

Measurement error modeling and assessment are crucial to any assay method. Realistic error models prioritize future efforts to reduce key error components and provide a way to estimate total ("random" and "systematic") measurement error. This paper describes multiple-component error models for radiation-based assay, gives example applications, and describes strategies for choosing and then fitting error models.

Statistical evaluation of FRAM gamma-ray isotopic analysis data.

High-purity germanium (HPGe) detector gamma-ray spectra were analyzed using the FRAM (fixed energy, response function analysis with multiple efficiencies) gamma-ray isotopic analysis software. The analyses are based on multiple measurements of samples having well-documented isotopic composition from mass spectrometry measurements. Statistical analyses of the FRAM results are reported, the errors in FRAM analyses arising from the choice of detector type and the energy region are discussed, and the errors that resulted from sample-dependent and analysis-dependent effects are quantified.

Maximally selected measures of evidence of disease clusters.

It is now common to read reports such as 'city A has a childhood cancer rate 30 per cent higher than the national average'. Because the details about how the data was examined can greatly impact the conclusions, the epidemiologist then wants to know more information, such as 'over what time period' and 'how many other cancer types were examined'. For example, city A might have calculated cancer rates for many age groups within different regions of the city over many time intervals, but reported only the highest cancer rate discovered in a particular group. We will refer to such selective reporting as 'maximally selecting measures of evidence of disease clustering', or less formally as 'fishing for statistical significance'. The objective of this paper is to study the behaviour of maximally selected statistics for measuring the extent of clustering in disease outbreaks. The original data is the time and location of each reported case of the disease. In some cases we are only given aggregates of the original data, such as the number of cases during a time period over a given region. We introduce new and review existing methods for correcting for the effect of making maximal selections in disease cluster detection. We consider three main cases with examples. We demonstrate via simulation and analytical approximations that some types of 'fishing' are simple to correct for while others are not.

Quasi-equilibrium theory for the distribution of rare alleles in a subdivided population: justification and implications.

This paper examines a quasi-equilibrium theory of rare alleles for subdivided populations that follow an island-model version of the Wright-Fisher model of evolution. All mutations are assumed to create new alleles. We present four results: (1) conditions for the theory to apply are formally established using properties of the moments of the binomial distribution; (2) approximations currently in the literature can be replaced with exact results that are in better agreement with our simulations; (3) a modified maximum likelihood estimator of migration rate exhibits the same good performance on island-model data or on data simulated from the multinomial mixed with the Dirichlet distribution, and (4) a connection between the rare-allele method and the Ewens Sampling Formula for the infinite-allele mutation model is made. This introduces a new and simpler proof for the expected number of alleles implied by the Ewens Sampling Formula.