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Rotation group bias (RGB) and discontinuities are in fact similar phenomena – both are systematic differences in non-sampling errors in a survey. Therefore, they can be dealt with using common approaches, and in this chapter, the authors review the methods for planning a transition to a new approach, and estimating and adjusting for the changes in the evolution of the series. The authors also review the available methods to quantify discontinuities induced by redesigns of the survey process. They show how time series models can be fitted to the outputs from rotating panels to account for RGB. The authors extend these times series models to estimate discontinuities. They also present some example applications. There are clear indications that the RGB is time dependent. This suggests that the common assumption that RGB can be ignored since it does not affect period-to-period change under a balanced rotation pattern is questionable.

We investigate use of two or more linked registers, or lists, for both population size estimation and to investigate the relationship between variables appearing on all or only some registers. This relationship is usually not fully known because some individuals appear in only some registers, and some are not in any register. These two problems have been solved simultaneously using the EM algorithm. We extend this approach to estimate the size of the indigenous M\=aori population in New Zealand, leading to several innovations: (1) the approach is extended to four registers (including the population census), where the reporting of M\=aori status differs between registers; (2) some individuals in one or more registers have missing ethnicity, and we adapt the approach to handle this additional missingness; (3) some registers cover subsets of the population by design. We discuss under which assumptions such structural undercoverage can be ignored and provide a general result; (4) we treat the M\=aori indicator in each register as a variable measured with error, and embed a latent class model in the multiple system estimation to estimate the population size of a latent variable, interpreted as the true M\=aori status. Finally, we discuss estimating the M\=aori population size from administrative data only. Supplementary materials for our article are available online., Comment: 34 pages, 4 figures, submitted to JASA

Background and Aims: The total concentration of copper (Cu) in wine cannot be routinely measured without relatively expensive equipment, reagents and/or without labour-intensive sample pretreatment. The following research describes an approach for the colorimetric measurement of total Cu in wine utilising a spectrophotometer and relatively cheap reagents. Method and Results: The proposed method is based on using bicinchoninic acid as the complexing chromophore for Cu. Copper was measured directly and rapidly in white wine, while a digestion of red wine was required. A key step for the analysis in the non-digested white wine matrix was the addition of silver(I) to aid the dissociation of Cu in the wine from sulfide. For red and white wines, the limit of detection was 0.02 mg/L Cu, and the repeatability was 3 ± 1%. The accuracy of the method was assessed in terms of recovery experiments (102 ± 3%, n = 4), and also by comparing total Cu concentration in 84 wines determined by the colorimetric method with that obtained by inductively coupled plasma with optical emission spectroscopy (R2 = 0.95). Conclusions: The colorimetric determination of total Cu was successfully optimised and validated in white and red wines. Significance of the Study: The methodology will allow wineries and researchers to routinely measure total Cu concentration in wine with a spectrophotometer. © 2020 Australian Society of Viticulture and Oenology Inc.

We investigate the situation where two or more registers, or lists, of individuals are linked both for the purpose of population size estimation and to investigate the relationship between variables appearing on all or only some of the registers. There is usually no full picture of this relationship because there are individuals that are in only some of the lists, and also individuals that are in none of the lists. These two problems have been solved simultaneously in dual system estimation using the EM algorithm. We extend this approach to four registers (including the population census) to estimate the size of the indigenous Māori population in New Zealand, where the reporting of Māori is not the same in each register and where there is a further missing data problem, with individuals included in one or more registers who did not provide their ethnicity. We consider the implications for estimating the size of the Māori population from administrative data only.