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Why are women not promoted as quickly or as high as men? Why are women paid less than their male counterparts for similar roles and responsibilities? Why is bridging the gender gap so difficult? For instance, in European Union countries in 2020 women’s gross hourly earnings were on average 13% below those of men, and 2018 US Census Bureau data says women of all races earned, on average, just 82 cents for every dollar earned by men of all races. Gender disparities, though, are much more severe and pervasive, as stated in the latest UN Women report (bit.ly/3Kc7knX): “Gender inequalities and discrimination filter through every issue, whether a new pandemic or longstanding conflicts, deep-seated disparities in income or a lack of political voice. Women and girls confront additional risks and obstacles simply because they are women and girls.” Gender equality, in all aspects of women’s and men’s lives, is a democratic value and a prominent sustainable development goal in the United Nations (UN) Agenda 2030. Gendered statistical reasoning and gender statistics play a crucial role in informing policies and the efforts towards achieving equality, as well as in allocation of resources, and the potential to effectively contribute towards the protection and advancement of the rights of all women and girls. In this article we illustrate the importance of improving the measurement, monitoring, and prediction of gender gaps by exploring the potential of Bayesian networks.

The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high- quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere dis- aggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for full exploiting gender-sensitive statis- tics, both standard and big data, though, faces some levels of criticality. The traditional descriptive linear combinations of a collection of simple indica- tors yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspec- tive stems from recent developments of Multivariate Latent Markov Models (MLMMs), suitable to express a latent characteristic both in time and space. In addition to introducing covariates, on any measurement scales, not only in the structural model bul also the measurement one, MLMMs are innovative in so that they can handle a vast mass of data from very different sources. Thus they lead the way to an extensive investigation of the gender gap, account- ing for apparent contradictions in rankings and hence highlighting different paths, or “transition”, toward a more equitable society.

In this paper, a class of resampling techniques for finite populations under $\pi $ps sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a “pseudo-population” on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the “sampling design level”. Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.

A collection of six novel bootstrap algorithms, applied to probability-proportional-to-size samples, is explored for variance estimation, confidence interval and p-value production. Developed according to bootstrap fundamentals such as the mimicking principle and the plug-in rule, these algorithms make use of an empirical bootstrap population informed by sampled units each with assigned weight. Starting from the natural choice of Horvitz–Thompson (HT)-type weights, improvements based on calibration to known population features are fostered. Focusing on the population total as the parameter to be estimated and on the distribution of the HT estimator as the target of bootstrap estimation, simulation results are presented with the twofold objective of checking practical implementation and of investigating the statistical properties of the bootstrap estimates supplied by the algorithms explored.

The effect of the practice of rounding non-integer weights in bootstrapping complex samples from finite populations is investigated by means of an extended simulation study. The extent to which rounding can interfere with basic Bootstrap principles as well as with the formal properties of the final Bootstrap estimates are evaluated. Indications and recommendations on application of this practice are discussed.

This book contains a selection of the papers presented at the ITACOSM 2013 Conference, held in Milan in June 2013. It is intended as an internationalforum of scientific discussion on the developments of theory and application of survey sampling methodologies and applications in human and natural sciences. The book gathers research paperscarefully selected from both invited and contributed sessions of the conference. The whole book appears to bearelevant contribution to various key aspects of sampling methodology and techniques; it dealswith some hot topics in sampling theory, such ascalibration, quantile-regression and multiple frame surveys and with innovative methodologies in important topics of both sampling theory and applications. Contributions cut across current sampling methodologies such as interval estimation for complex samples, randomized responses, bootstrap, weighting, modeling, imputation, small area estimationand effective use of auxiliary information; applications cover a wide and enlarging range of subjects in official household surveys, Bayesian networks,auditing,business and economicsurveys, geostatistics and agricultural statistics. The book is an updated, high level reference surveyaddressed to researchers, professionals and practitioners in many fields.

Writing papers is an essential part of the research process. Researchers have a professional obligation of disseminating their results, making them available for others to use to enhance common scientific knowledge. Besides the fun of sharing their own ideas and views, to publish is essential in order to actually have a scientific career. Although scientific writing certainly has its own conventions and standards, I suspect there is no a unique true recipe making the trick. As a matter of fact I do not have any. However my quite long time in the academic arena has given me a pretty clear idea about how I do and do not like things done. In this paper I will be giving my personal view and rules, in the hope that sharing my own experience would do some good to others as it did for me.

The problem of bootstrapping the estimator’s variance under a probability proportional to size design is examined. Focusing on the Horvitz-Thompson estimator, three πPS-bootstrap algorithms are introduced with the purpose of both simplifying available procedures and of improving efficiency. Results from a simulation study using both natural and artificial data are presented in order to empirically investigate the properties of the provided bootstrap variance estimators.

Center sampling is useful in finite population surveys when exhaustive lists of all units are not available and the target population is naturally clustered into a number of overlapping sites spread over an area of interest such as, for instance, the immigrant population illegally resident in a country. Center sampling has been successfully employed in official European surveys; nevertheless few systematic theoretical results have been given yet to support empirical findings. In this paper a general theory for Center sampling is formalized and an unbiased estimator for the mean of a quantitative or dichotomous characteristic is proposed together with its exact variance. A suitable estimator for the variance, unbiased under simple random sampling, is also derived and the optimum allocation of the sample size among centers subject to linear cost constraints is discussed. Other sampling designs, useful under operational aspects, are also considered., Statistica; Vol 63, No 3 (2003); 537-560

Multiple Frame Survey has been originally proposed, according to an optimality approach, in order to persecute survey cost savings, especially in the case of a complete list available but expensive to sample. In the modern sampling practice it is frequent the case where a total and up-to-date list of units, to be used as sampling frame, is not available or it may not be built unless expensive or unfeasible screening of the target population. Instead, a set of lists singularly partial, usually overlapping, with union offering an adequate coverage of the target population, can be available. Thus the collection of the partial lists can be used as Multiple Frame. Literature about Multiple Frame estimation theory mainly concentrates over the Dual Frame case and it is only rarely concerned with the important practical issue of the variance estimation. By using a multiplicity approach a Single Frame estimator is proposed. The new estimator naturally applies to any number of frame and it is very simple so that its variance is given exactly and easily estimated.