International Statistical Review最新文献

This paper proposes an instrumental variable regression estimator of population totals using a sample, a set of links between the sample units and records on an administrative file, and a set of calibration totals calculated from the administrative file. This paper proposes a survey-weighted estimator of a population total that is valid when the survey non-response mechanism is non-ignorable and false negatives occur in the administrative-survey linkage. False negatives lead to measurement error in the administrative variables that are available on the survey and will lead to biased estimates if not taken into account. We show the benefit of the proposed approach in a simulation and in a case study.

John M. Abowd is the Chief Scientist and Associate Director for Research and Methodology, US Census Bureau. He completed his AB in Economics at Notre Dame in 1973 and his PhD in Economics at University of Chicago in 1977 under Arnold Zellner. During his academic career, John has held faculty positions at Princeton, the University of Chicago, and, since 1987 at Cornell University where he is the Edmund Ezra Day Professor Emeritus of Economics, Statistics and Data Science. John was trained as a statistician and labor economist, and his economic research has focused on the rigorous empirical evaluation of labor market institutions. In the late 1990s, he began working with the Census Bureau on projects that would end up leveraging administrative and survey records into official statistical products. Through that work, he has developed a research agenda focused on issues necessary to generate those products, including data privacy, synthetic data, total error analysis, data linkage, and missing data problems, among others.

This article serves two purposes. Firstly, it surveys the Bandt and Pompe methodology for the statistical community, stressing topics that are open for research. Secondly, it contributes towards a better understanding of the statistical properties of that approach for time series analysis. The Bandt and Pompe methodology consists of computing information theory descriptors from the histogram of ordinal patterns. Such descriptors lie in a 2D manifold: the entropy–complexity plane. This article provides the first proposal of a test in the entropy–complexity plane for the white noise hypothesis. Our test is based on true white noise sequences obtained from physical devices. The proposed methodology provides consistent results: It assesses sequences of true random samples as random (adequate test size), rejects correlated and contaminated sequences (sound test power) and captures the randomness of generators previously analysed in the literature.

Although cloud seeding is a commonly used and plausible method for rainfall enhancement, its practical efficacy has not been established for seeding of convective clouds with hygroscopic materials. Other methods of rainfall enhancement are viewed as much less plausible. Thus, although increased electrical charge has been shown to enhance precipitation in cloud chamber experiments, exactly how ionisation of clouds can increase rainfall in the open atmosphere remains conjectural. A trial of the efficacy of ionisation for rainfall enhancement in the Hajar Mountains of Oman was carried out over 2013–2018. This paper provides some background to this non-mainstream approach to increasing rainfall, showing how statistical modelling of rainfall data might be used to nudge rainfall enhancement via ionisation towards a more scientifically acceptable status. Analysis of the data collected in the trial shows that ionisation led to a statistically significant enhancement in positive rainfall in gauges located up to 70 km downwind of the ionisers. A headline analysis specified prior to commencement of the trial resulted in an estimate of 16.23% enhancement relative to rainfall that would have fallen without any ionisation, while a more sophisticated after the event analysis increased this estimate to 17.64%.