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When many statistical hypotheses are evaluated simultaneously, statisticians often recommend adjusting (or "correcting") standard hypothesis tests. In this article, I (1) distinguish two senses of adjustment, (2) investigate the prudential and epistemic goals that adjustment might achieve, and (3) identify conditions under which a researcher should not adjust for multiplicity in the two senses I identify. I tentatively conclude that the goals of scientists and the public may be misaligned with the decision criteria used to evaluate multiple-testing regimes. [ABSTRACT FROM AUTHOR]

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying two theorems attributed to Alan Turing and Jack Good. The procedure entails simulating data sets under two hypotheses, calculating Bayes factors, and assessing whether their expected values align with theoretical expectations. We illustrate this method with an ANOVA example and a network psychometrics application, demonstrating its efficacy in detecting calculation errors and confirming the computational correctness of the Bayes factor results. This structured validation approach aims to provide researchers with a tool to enhance the credibility of Bayes factor hypothesis testing, fostering more robust and trustworthy scientific inferences. [ABSTRACT FROM AUTHOR]

Stepwise multiple testing procedures have attracted several statisticians for decades and are also quite popular with statistics users because of their technical simplicity. The Bonferroni procedure has been one of the earliest and most prominent testing rules for controlling the familywise error rate (FWER). A recent article established that the FWER for the Bonferroni method asymptotically (i.e., when the number of hypotheses becomes arbitrarily large) approaches zero under any positively equicorrelated multivariate normal framework. However, similar results for the limiting behaviors of FWER of general stepwise procedures are nonexistent. The present work addresses this gap in a unified manner by elucidating that, under the multivariate normal setups with some correlation structures, the probability of rejecting one or more null hypotheses approaches zero asymptotically for any step-down procedure. Consequently, the FWER and power of the step-down procedures also tend to be asymptotically zero. We also establish similar limiting zero results on FWER of other popular multiple testing rules, e.g., Hochberg's and Hommel's procedures. It turns out that, within our chosen asymptotic framework, the Benjamini–Hochberg method can hold the FWER at a strictly positive level asymptotically under the equicorrelated normality. [ABSTRACT FROM AUTHOR]

A critically important balance in educational measurement between practical concerns and matters of technique has atrophied in recent decades, and as a result, some important issues in the field have not been adequately addressed. I start with the work of E. F. Lindquist, who exemplified the balance that is now wanting. Lindquist was arguably the most prolific developer of achievement tests in the history of the field and an accomplished statistician, but he nonetheless focused extensively on the practical limitations of testing and their implications for test development, test use, and inference. I describe the withering of this balance and discuss two pressing issues that have not been adequately addressed as a result: the lack of robustness of performance standards and score inflation. I conclude by discussing steps toward reestablishing the needed balance. [ABSTRACT FROM AUTHOR]

AbstractA clinical trial aims to uncover high-quality evidence for both efficacy and safety for an experimental treatment. Traditionally, statisticians evaluate the two aspects marginally, where, for example, efficacy considers the proportion of patients cured and safety considers the proportion of patients not showing adverse events. However, separate analyses could end up with misleading results, as the cumulative nature of clinical outcomes and the correlation between efficacy and safety endpoints are neglected. The desirability of outcome ranking (DOOR) method addresses such issues and provides a patient-centric approach to benefit:risk evaluation. A patient’s outcome is ranked based on pre-specified clinical criteria, where the most desirable rank represents a good outcome with no side effects and the least desirable rank is the worst possible clinical outcome. As the DOOR outcome is a temporal state that can have repeated measures, we propose a longitudinal approach that estimates and infers the temporal treatment effects. We develop a methodology for constructing simultaneous confidence bands by accounting for the correlations across time points. Additionally, we propose a weighted Mann-Whitney-U statistic to evaluate the treatment effect over the entire trial period. The performance of the proposed methodologies is examined through simulations and an application to a COVID-19 trial. [ABSTRACT FROM AUTHOR]

In June–July 2022, the Randomization Working Group () conducted an online survey on the current practices in the application of randomization in clinical trials. Of 145 unique respondents, 137 (94.5%) identified themselves as statisticians. The majority of respondents were from academia and pharmaceutical companies. Permuted block randomization, with or without stratification, was the most frequently chosen method of randomization for RCTs. Interactive Web-Based Response Systems (IWRS) and "in-house" or a combination of "in-house and outsourced" randomization models were found to be most common in practice. Over 80% of respondents perceived some challenges to adoption of new randomization methods that may have more desirable properties. Over 80% of respondents identified opportunities for improving current practice, including education/training, development of standards/guidance on randomization, and adoption of validated software for generating randomization sequences. In summary, practitioners acknowledge the pivotal role of randomization in clinical trials. There are some perceived challenges to successful implementation of randomization, and there are opportunities for improving practice. [ABSTRACT FROM AUTHOR]

Digitalization has created a world awash with data from sources almost unimaginable 50 years ago. Using recent examples from climate change, the COVID-19 pandemic, official statistics, and artificial intelligence, the address will illustrate the importance of understanding the data generating process and the data about the data. It will be argued that statisticians and other data professionals have an increasingly important role to play as advocates for well-defined and well documented data generation—particularly as the distinction between inputs and outputs becomes blurred. For the Society, this does not represent a shift away from statistics, rather it re-establishes the link to our roots. [ABSTRACT FROM AUTHOR]

AbstractKeywords are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic keyword co-occurrence network and propose a latent space model for analyzing it. Our model has two special characteristics. First, it is applicable to weighted networks; however, most previous models were primarily designed for unweighted networks. Simply replacing the frequency of keyword co-occurrence with binary values would result in a significant loss of information. Second, our model can handle the situation where network nodes evolve over time, and assess the effect of new nodes on network connectivity. We use the projected gradient descent algorithm to estimate the latent positions and establish the theoretical properties of the estimators. In the real data application, we study the keyword co-occurrence network within the field of statistics. We identify popular keywords over the whole period as well as within each time period. For keyword pairs, our model provides a new way to assess the association between them. Finally, we observe that the interest of statisticians in emerging research areas has gradually grown in recent years. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

AbstractHeavy tails (HT) arise in many applications and their presence can disrupt statistical inference, yet the HT statistical literature requires a theoretical background most practicing statisticians lack. We provide an overview of the influence of HT on the performance of basic statistical methods and useful theorems aimed at the practitioner encountering HT in an applied setting. Higher or even lower product moments (i.e., variance, skewness, etc.) can be infinite for some HT populations, yet all L-moments are always finite, given that the mean exists, thus, the theory of L-moments is uniquely suited to all HT distributions and data. We document how L-kurtosis, (a kurtosis measure based on the fourth L-moment) provides a general and practical heaviness index for contrasting tail heaviness across distributions and datasets and how a single L-moment diagram can document both the prevalence and impact of HT distributions and data across disciplines and datasets. Surprisingly, the theory of L-moments, an extension and evolution of probability weighted moments, has been largely overlooked by the literature on HT distributions that exhibit infinite moments. Experiments reveal L-kurtosis ranges under which various HT distributions result in mild to severe disruption to the bootstrap, the central limit theorem (CLT), and the law of large numbers, even for distributions which exhibit finite product moments. [ABSTRACT FROM AUTHOR]

ABSTRACT Gross domestic product (GDP) is frequently used as a proxy for well‐being. Such use of GDP is problematic for many reasons, for GDP excludes activities that contribute to well‐being and includes others that have a negative impact instead. As a result, a vast array of metrics has been developed to complement or replace it and put well‐being at the heart of policymaking. Nonetheless, previous research has shown that their use and impact on policymaking has been limited. This article examines the use and impact of well‐being metrics according to their own developers and intended users in the crucial cases of Scotland and Italy, focusing specifically on the two countries' official well‐being frameworks. Despite being at the forefront of the well‐being debate, both countries have never been studied in this regard before. This article fills this gap, collating views from more than 100 stakeholders, including statisticians, members of interest groups, policymakers and journalists. Findings show that the vast majority of informants could not cite any examples of cases in which either framework impacted on policymaking, or in which they themselves had used these. In some cases, this was due to them not being aware of what such frameworks were in the first place. Those who could identify some examples were those who were or used to be part of the government. Examples would, however, tend to be vague, in some instances remarkable yet merely anecdotal, and still in others the result of an ‘ex‐post rationalisation’. [ABSTRACT FROM AUTHOR]

We appreciate Cro et al.'s efforts to bring wider attention to the debate surrounding variance estimation for reference‐based imputation methods. However, we believe that the way this debate is presented as "multiple imputation" versus "conditional mean imputation" can be misleading. Both of these imputation methods rely on identical assumptions and provide essentially identical treatment effect estimates. While conditional mean imputation naturally focuses on the frequentist repeated sampling variance, we show here that it can be easily adapted to target a variance with similar properties to Rubin's variance. Therefore, conditional mean imputation combined with jackknife resampling remains a valid and effective deterministic method for handling missing data under missing‐at‐random or reference‐based assumptions regardless of the user's preference for variance estimation. We also reappraise the frequentist variance by arguing that it correctly reflects the strong assumptions of reference‐based imputation. In contrast, we are not aware of any frequentist or Bayesian framework under which Rubin's variance provides correct inference. [ABSTRACT FROM AUTHOR]

Accurate frequentist performance of a method is desirable in confirmatory clinical trials, but is not sufficient on its own to justify the use of a missing data method. Reference‐based conditional mean imputation, with variance estimation justified solely by its frequentist performance, has the surprising and undesirable property that the estimated variance becomes smaller the greater the number of missing observations; as explained under jump‐to‐reference it effectively forces the true treatment effect to be exactly zero for patients with missing data. [ABSTRACT FROM AUTHOR]

In recent years, researchers and statisticians have increasingly used econometric techniques to generate timely, high-quality, and detailed official statistics. This study presents a modified form of regression-based temporal disaggregation model to compile the index of production in construction for Türkiye, employing a state-space modeling approach. The model incorporates the twelve-month moving sum of deflated turnover as an observed variable and the number of employees as an exogenous variable. Finally, four alternative models--three assuming constant labor productivity and one assuming time-varying labor productivity--are compared. [ABSTRACT FROM AUTHOR]

Members of the ASA's Section on Statistical Consulting established the Pathways to Promotion Committee in 2021 to provide guidance and support for navigating a career as a collaborative statistician. In three years of existence, the Committee has produced seven webinars on relevant topics, each attended by more than one hundred participants. Committee members have given four oral presentations, organized three roundtables, led multiple discussions at ASA meetings and published four articles. These efforts have inspired, created and facilitated new connections for collaborative statisticians who feel isolated in their career path. This paper describes the formation and development of the Committee, reports its impact on the community of collaborative statisticians and discusses potential future directions. [ABSTRACT FROM AUTHOR]

Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science and library sciences are needed to support these research systems and promote high‐calibre research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centres that host skilled experts who approach research with best practices in mind and domain‐specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long‐term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency and long‐term sustainability. We hope this work contributes to—and provides shared language for—a conversation on a broader framework beyond metrics that can be used to advocate for needed resources. [ABSTRACT FROM AUTHOR]

Professor (now emeritus) Nils Lid Hjort has through more than four decades been one of the most original and productive statisticians in Norway, contributing to a wide range of topics such as survival analysis, Bayesian nonparametrics, empirical likelihood, density estimation, focused inference, model selection, and confidence distributions. This conversation, which took place at the University of Oslo in December 2023, sheds light on how Nils Hjort's curious and open mind, coupled with a deep understanding, has enabled him to seamlessly navigate between different fields of statistics and its applications. Our aim is to encourage the statistics community to always be on the lookout for unexpected connections in statistical science and to embrace unexpected encounters with fellow statisticians from around the world. [ABSTRACT FROM AUTHOR]

Background and Study Aim The attack is a crucial technical element in scoring points in volleyball. This research aims to identify differences in attack efficiency and errors based on the value level of teams in the competitive system, considering different playing positions. Material and Methods The analysis included 86 attacking players from the rosters of 12 teams in the men's domestic first league. The distribution by positions was as follows: 37 outside hitters, 19 opposites, and 30 middle blockers. Statisticians from each team recorded the data using Data Volley software during three championship matches. For each player, the following parameters were interpreted: Attack Efficiency (E%), Error% (=), Blocked Attack% (/), Poor% (-), Blocked but Recovered% (!), Positive% (+), and Winning% (#). Attack efficiency was analyzed for each playing position across top teams (positions 1-4 in the final ranking), mid-level teams (positions 5-8), and lower-level teams (positions 9-12). Results Across all three playing positions, top teams demonstrate higher percentages in attack efficiency, winning executions, and positive outcomes compared to average and lower-level teams. Top and average teams also have lower percentages of errors in attack executions. For the outside hitter position, no statistically significant superiority is observed between top and middle teams for any attack variable, indicating a balance in player performance. In the opposite hitter position, top and middle-level players show higher attack efficiency and direct point scores (Winning). They also have lower poor execution rates, despite the lack of statistical confirmation for superiority in many attack variables. Among all positions, top middle blockers exhibit a clear superiority over low-level teams across all attack variables. Conclusions The findings of the study highlight the critical role of team value level in influencing attack efficiency and execution quality across different playing positions in volleyball. The results underscore the importance of strategic differentiation in training and game planning. This is especially crucial for teams aiming to optimize their performance. Coaches and trainers should focus on tailored approaches that address the specific strengths and weaknesses of each playing position. They should also consider the overall team value level in their strategies. This approach could lead to improved competitive outcomes and a more effective utilization of player potential across all levels of competition. [ABSTRACT FROM AUTHOR]

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Summary Probability weights have been widely used in addressing selection bias arising from a variety of contexts. Common examples of probability weights include sampling weights, missing data weights, and propensity score weights. Frequency weights, which are used to control for varying variabilities of aggregated outcomes, are both conceptually and analytically different from probability weights. Popular software such as R, SAS and STATA support both types of weights. Many users, including professional statisticians, become bewildered when they see identical estimates, but different standard errors and p$$ p $$‐values when probability weights are treated as frequency weights. Some even completely ignore the difference between the two types of weights and treat them as the same. Although a large body of literature exists on each type of weights, we have found little, if any, discussion that provides head‐to‐head comparisons of the two types of weights and associated inference methods. In this paper, we unveil the conceptual and analytic differences between the two types of weights within the context of parametric and semi‐parametric generalised linear models (GLM) and discuss valid inference for each type of weights. To the best of our knowledge, this is the first paper that looks into such differences by identifying the conditions under which the two types of weights can be treated the same analytically and providing clear guidance on the appropriate statistical models and inference procedures for each type of weights. We illustrate these considerations using real study data. [ABSTRACT FROM AUTHOR]

ABSTRACT Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh‐Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture‐process variable design is given as a case study. [ABSTRACT FROM AUTHOR]

Background: scapular dyskinesis and scapular muscle weakness are condition associated with upper limb activity, poor posture, sustained manual labor. This cause discomfort and pain. Sugar factory workers frequently engage in repetitive activity that can cause scapular dyskinesis. Methods: The cross-sectional survey that was conducted in Ajinkya tara sugar factory satara the purpose of this repetitive study is to determine how many sugar factory workers are affected with scapular dyskinesis. Prior to conducting the survey, the ethical committee approval was made. Patient select according to inclusion and exclusion criteria. With permission.172 sugar factory workers are participating in this study. And result was obtained by the help of statistician Result: in this study we found 128 sugar factory workers are positive for scapular dyskinesis the percentage of prevalence of this study was 74.41%and also these workers are present with scapular muscle weakness and present with shoulder pain. Conclusion: this study indicates that SD is prevalent in sugar factory worker. Those are engaged in repetitive overhead activity and heavy lifting. And also, scapular muscle weakness seen in this people. [ABSTRACT FROM AUTHOR]

ABSTRACT Chemistry, manufacturing, and control (CMC) statisticians play a key role in the development and lifecycle management of pharmaceutical and biological products, working with their non‐statistician partners to manage product quality. Information used to make quality decisions comes from studies, where success is facilitated through adherence to the scientific method. This is carried out in four steps: (1) an objective, (2) design, (3) conduct, and (4) analysis. Careful consideration of each step helps to ensure that a study conclusion and associated decision is correct. This can be a development decision related to the validity of an assay or a quality decision like conformance to specifications. Importantly, all decisions are made with risk. Conventional statistical risks such as Type 1 and Type 2 errors can be coupled with associated impacts to manage patient value as well as development and commercial costs. The CMC statistician brings focus on managing risk across the steps of the scientific method, leading to optimal product development and robust supply of life saving drugs and biologicals. [ABSTRACT FROM AUTHOR]

Under ever-increasing pressure to provide more with less, to justify budgets and to earn public trust, official statistics has long been concerned with how to better prove and communicate its value. Being statisticians, our inclination has been to express the value of our offer in quantitative terms. But an ONS-led Task Force under the Conference of European Statisticians (CES) argued that before we can quantify 'the value of official statistics' we need to understand what this really means. This entails first articulating our own central goals as providers of a public good and then working outwards from these goals to formulate the means of fulfilling them. Only then can we start to define measurable indicators of achievement to assess how far we are creating this intended value. This is the reverse of the process often followed, which starts out by identifying already-available indicators and tries to determine the aspects of value of which they are indicative. Future international work should focus on developing tools for better understanding the pathways from goals to value indicators; sharing experiences of efforts to prove and improve the value of official statistics; and developing a core set of measures using the methods outlined in the article. [ABSTRACT FROM AUTHOR]

This narrative review describes our experience of working with Doug Altman, the most highly cited medical statistician in the world. Doug was particularly interested in diagnostics, and imaging studies in particular. We describe how his insights helped improve our own radiological research studies and we provide advice for other researchers hoping to improve their own research practice. • We describe how working with the statistician Doug Altman improved our research. • Medical statisticians should be consulted when preparing for research. • Medical statisticians help design unbiased, adequately powered studies. • High quality medical research is unlikely without statistical help. [ABSTRACT FROM AUTHOR]

Under ever-increasing pressure to provide more with less, to justify budgets and to earn public trust, official statistics has long been concerned with how to better prove and communicate its value. Being statisticians, our inclination has been to express the value of our offer in quantitative terms. But an ONS-led Task Force under the Conference of European Statisticians (CES) argued that before we can quantify 'the value of official statistics' we need to understand what this really means. This entails first articulating our own central goals as providers of a public good and then working outwards from these goals to formulate the means of fulfilling them. Only then can we start to define measurable indicators of achievement to assess how far we are creating this intended value. This is the reverse of the process often followed, which starts out by identifying already-available indicators and tries to determine the aspects of value of which they are indicative. Future international work should focus on developing tools for better understanding the pathways from goals to value indicators; sharing experiences of efforts to prove and improve the value of official statistics; and developing a core set of measures using the methods outlined in the article. [ABSTRACT FROM AUTHOR]

Statistical collaboration requires statisticians to work and communicate effectively with nonstatisticians, which can be challenging for many reasons. To identify common themes and lessons for working smoothly with nonstatistician collaborators, two focus groups of primarily academic collaborative statisticians were held. We identified qualities of collaborations that tend to yield fruitful relationships and those that tend to yield nothing (or worse, with one or both parties being dissatisfied). The initial goal was to share helpful knowledge and individual experiences that can facilitate more successful collaborative relationships for statisticians who work within academic statistical collaboration units. These findings were used to design a follow‐up survey to collect perspectives from a wider set of practicing statisticians on important qualities to consider when assessing potential collaborations. In this survey of practicing statisticians, we found widespread agreement on many good and bad qualities to promote and discourage, respectively. Interestingly, some negative and positive collaboration qualities were less agreed upon, suggesting that in such cases, a mix‐and‐match approach of domain experts to statisticians could alleviate friction and statistician burnout in team science settings. The perceived importance of some collaboration characteristics differed between faculty and staff, while others depended on experience. [ABSTRACT FROM AUTHOR]

Academic research productivity relies upon the contribution of statisticians, who are typically clustered in statistics and biostatistics departments, isolated from clinical researchers. Most academic health centres have created consultation hubs or research incubators to make statisticians available for individual collaboration to support the clinical research enterprise. Additionally, some clinical departments within academic health centres have recognized the value in colocating statisticians within their clinical departments to improve availability for collaboration with physicians/researchers. Embedded statisticians encounter the same challenges of isolated statisticians regarding professional support and networking, mentorship and clear role expectations. While for all collaborative statisticians, it is important to effectively communicate value to both collaborators and supervisors, this may be especially problematic for embedded statisticians in clinical departments where their supervisors may not have backgrounds in research or statistics. Previous papers have reported valuable metrics for statisticians, particularly those associated with Biostatistics, Epidemiology and Research Design Cores. There is a knowledge gap regarding metrics tailored to meet the needs of the embedded statistician and clinical supervisors. This paper is a first step towards addressing this important need. In this paper, we explore (1) the critical role of collaborative statisticians and the benefits and challenges of the embedded statistician model, (2) the need for additional metrics specific to embedded statisticians which measure value and (3) how to design a value report. We offer a framework for evaluation of the contributions of the embedded statistician with the following domains: (1) collaboration, (2) research output/productivity, (3) mentoring and (4) education. Metrics that are particularly specific to embedded statisticians and that are not routinely captured include time from project initiation to completion/outcome, time from initial statistical consultation to statistical outcome completion and summary of level of contribution for manuscripts and presentations in addition to author order. We conclude with thoughts on future directions for development of metrics and reporting systems for statisticians embedded in clinical departments. [ABSTRACT FROM AUTHOR]

Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the "c-value" as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In the 21st International Conference of Labour Statisticians (ICLS) convened in October 2023, an amendment to the 19th ICLS resolution of 2013 on labour underutilisation statistics was ratified. It introduces refined measures for assessing labour underutilisation, providing nations with updated guidelines for integrating these measures into their statistical frameworks. Notably, it introduces the concept of the potential labour force, which includes individuals not currently employed, who might be disinclined or unable to seek employment due to various constraints. In our study, we specifically address this segment within the Indian female youth population, referring to them as the "missing labour force". Utilising India's official national datasets—the Employment-Unemployment Survey for 2011–12 and the Periodic Labour Force Survey (PLFS) from 2017–18 to 2022–23—we calculate the absolute size of the female youth population comprising the missing (or potential) labour force between 2011–12 and 2022–23. Our analysis delineates the magnitude of this issue, presenting both national and regional dimensions. In 2022–23, approximately 114.6 million females aged 15 to 35, were missing from the labour market, accounting for 48% of the total female youth population in India. Acknowledging the current challenges in the labour market, particularly the high rates of informality and unemployment among female youth, this paper proposes strategic interventions and policy recommendations aimed at integration of the missing female youth labour force into employment sectors. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves managing risk, and statisticians are ideally positioned to champion not only the derivation of metrics for individual projects, but also advocate decision‐making at a broader portfolio level. This article aims to examine the existing different portfolio decision‐making approaches and to suggest opportunities for statisticians to add value in terms of introducing probabilistic thinking, quantitative decision‐making, and increasingly advanced methodologies. [ABSTRACT FROM AUTHOR]

The full extent of feminicide in Mexico remains unknown. When available, data on the gender-related killing of women and girls are often incomplete, inaccurate, or inexplicable. In this article, a sociologist (Saide) and a statistician (Maria) query feminicide data in Mexico. Drawing on Timnit Gebru et al.'s 'datasheets for datasets' and Sarah Holland et al.'s 'data nutrition label' frameworks, we zoom in on the two primary governmental sources measuring feminicide in the country, the mortality records processed by the Instituto Nacional de Estadística, Geografía e Informática (INEGI) and the alleged feminicide investigation files published by the Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública (SESNSP). In the discussion, we shed light on two noteworthy remarks. First, the discordance between INEGI and SESNSP data, whereby we outline four crucial variations: naming, underreporting, comparability, and availability. Second, the shortcomings of these data sources in measuring feminicide as we understand it sociologically. In other words, neither explicitly gauge the 'gender-related' motivation underlying the crime. Instead, what data from INEGI and SESNSP currently provide us with are discordant approximations of the phenomenon, aligning with what Sandra Walklate and Kate Fitz-Gibbon define as 'thin' feminicide counts. This contribution seeks to act as a guide to better understand feminicide data in Mexico, to enhance effective communication between data creators and users concerned with data-making practices, and to ignite the querying of data engaging with social justice and accountability against feminicide and beyond. [ABSTRACT FROM AUTHOR]

As the New Zealand government is progressing plans for substituting the census as we know it for information held in state agency records, the huge gap between what we need population statistics for, and the scope and quality of existing statistics stays under the radar. The scale and volatility of migration, the impact of increased longevity and the different demographic dynamics of fast-growing ethnic communities must be continually measured in ways we can trust. Confidence that the statistical qualities of the census would be replicated requires transparency, validation and independent peer review if trust is to be maintained. [ABSTRACT FROM AUTHOR]

On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives. [ABSTRACT FROM AUTHOR]

The rise of internet-based services and products in the late 1990s brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision making. Over the past two decades, organizations such as Airbnb, Alibaba, Amazon, Baidu, Booking.com, Alphabet's Google, LinkedIn, Lyft, Meta's Facebook, Microsoft, Netflix, Twitter, Uber, and Yandex have invested tremendous resources in online controlled experiments (OCEs) to assess the impact of innovation on their customers and businesses. Running OCEs at scale has presented a host of challenges requiring solutions from many domains. In this article we review challenges that require new statistical methodologies to address them. In particular, we discuss the practice and culture of online experimentation, as well as its statistics literature, placing the current methodologies within their relevant statistical lineages and providing illustrative examples of OCE applications. Our goal is to raise academic statisticians' awareness of these new research opportunities to increase collaboration between academia and the online industry. [ABSTRACT FROM AUTHOR]

In ecology, the observation process (how we collect data) can be as complex as the biological process we are investigating. Failure to account for complex observation processes leads to uncertainty, biased inference and poor predictions, resulting in misleading research results. Often, field scientists are best placed to describe observation problems that occur but are excluded from discussions about how to tackle these problems statistically. Statisticians are often unaware of the nuances of observation processes leading to the problems being ignored, or tackled on a case-by-case basis. We propose a typology of observation problems and inferential solutions, hence facilitating the linkages between field protocols and statistical treatments. Advances in statistics mean that it is now possible to tackle increasingly sophisticated observation processes. The intricacies and ambitious scale of modern data collection techniques mean that this is now essential. Methodological research to make inference about the biological process while accounting for the observation process has expanded dramatically, but solutions are often presented in field-specific terms, limiting our ability to identify commonalities between methods. We suggest a typology of observation processes that could improve translation between fields and aid methodological synthesis. We propose the LIES framework (defining observation processes in terms of issues of Latency, Identifiability, Effort and Scale) and illustrate its use with both simple examples and more complex case studies. [ABSTRACT FROM AUTHOR]

The way of thinking of mathematicians and chemists in their respective disciplines seems to have very different levels of abstractions. While the firsts are involved in the most abstract of all sciences, the seconds are engaged in a practical, mainly experimental discipline. Therefore, it is surprising that many luminaries of the mathematics universe have studied chemistry as their main subject. Others have started studying chemistry before swapping to mathematics or have declared some admiration and even love for this discipline. Here I reveal some of these mathematicians who were involved in chemistry from a biographical perspective. Then, I analyze what these remarkable mathematicians and statisticians could have learned while studying chemical subjects. I found analogies between code-breaking and molecular structure elucidation, inspiration for statistics in quantitative analytical chemistry, and on the role of topology in the study of some organic molecules. I also analyze some parallelisms between the way of thinking of organic chemists and mathematicians in terms of the use of backward analysis, search for patterns, and use of pictures in their respective researches. [ABSTRACT FROM AUTHOR]

The present work proposes a new two-stage unrelated randomized response model to estimate the mean number of individuals who possess a rare sensitive attribute in a given population by using Poisson probability distribution, when the proportion of rare non-sensitive unrelated attribute is known and unknown. The properties of the proposed model are examined. The variance of the proposed randomized response model smaller than Land et al. (Stat J Theor Appl Stat, 46(3):351–360, 2012) and Singh and Tarray (Model Assist Stat Appl, 10(2):129–138, 2015) to estimate sensitive characteristic under study. The proposed model provides a more efficient unbiased estimator of the mean number of individuals. The procedure also introduces the measure of privacy protection of respondents and compares randomized response models in terms of efficiency and privacy protection. Empirical illustrations are presented to support the theoretical results and suitable recommendations are put forward to the survey statisticians/practitioners. [ABSTRACT FROM AUTHOR]

The manuscript under consideration delves into a comprehensive exploration of the dual exponential ratio estimator, particularly in the context of non-response scenarios. In the following discourse, we will embark on an intricate journey through this research, emphasizing the pivotal aspects and findings that unravel the significance of this estimator in the realm of statistical estimation. The crux of this investigation revolves around evaluating the Mean Squared Error (MSE) and the Predictive Relative Efficiency (PRE) of the dual exponential ratio estimator. These two performance metrics serve as essential benchmarks for assessing the accuracy and effectiveness of the estimator. Notably, they play a crucial role in determining the estimator's suitability for practical applications, especially in situations where non-response is prevalent. To begin our exploration, it is imperative to understand the fundamental concept of the dual exponential ratio estimator. This estimator is a statistical tool employed in situations where traditional estimators may falter due to non-response, a phenomenon frequently encountered in surveys and data collection. It leverages a dual exponential model to address this challenge, making it a valuable addition to the toolkit of statisticians and researchers. The manuscript embarks on a rigorous theoretical analysis of the dual exponential ratio estimator's MSE and PRE. Through a series of mathematical derivations and proofs, the authors elucidate the underlying principles governing its performance. This theoretical foundation is crucial, as it not only establishes a solid framework for evaluating the estimator but also provides insights into its behavior under different conditions. However, theory alone can only take us so far. To validate the theoretical findings and assess the estimator's practical utility, numerical experiments are conducted. These experiments involve simulations and real-world data scenarios, allowing the authors to draw comparisons between the dual exponential ratio estimator and traditional estimators. The numerical results serve as a bridge between theory and application, offering empirical evidence of the estimator's prowess. In essence, this manuscript fills a critical gap in the field of statistical estimation by thoroughly investigating the dual exponential ratio estimator's performance in the presence of non-response. By juxtaposing its MSE and PRE with those of traditional estimators, it provides valuable insights into the potential advantages of adopting this novel approach. Moreover, the combination of rigorous theory and practical validation ensures that the findings are both intellectually sound and operationally relevant. The dual exponential ratio estimator, as explored and analyzed within these pages, emerges as a promising solution, backed by both theoretical rigor and empirical support. This research contributes not only to the theoretical foundations of statistics but also to its real-world applications, underscoring the estimator's potential to enhance the accuracy and reliability of estimation in the face of non-response complexities. [ABSTRACT FROM AUTHOR]

Curriculum change and the ready access to school level appropriate statistical software has seen the focus of statistical practice for novice statisticians move from primarily constructing graphs and calculating statistics to describing and reasoning from distributions. Many multi-faceted concepts and statistical ideas underpin distributions, which students find difficult to navigate and cognitively coordinate. Limited research, however, exists on how to enhance students' communication when describing distributions. This paper explores the actions of a teacher as she supported 14–15-year-old students to develop notions of distribution and to describe distributions. The findings indicated that the teacher, through knowing, modelling, and listening, supported the development of student statistical language and communication. Students, through engaging in specifically designed instructional activities to engineer learning around the concept of distribution, seem to be able to transition from using their own language to using statistical language to describe distributions that communicated the concepts and features that were identified in the distribution description framework. [ABSTRACT FROM AUTHOR]

Post-stratification is applied when the subpopulation membership is observed only for sampled values and the goal is to estimate stratum-specific parameters which leads the survey statisticians towards primary goals i.e., classification of non-sampled units into different strata and prediction of the values of the study variables. Regression models, on one side, optimize the prediction of the study variable's non-sampled values while the classification algorithms, on the other side, look for the classification of non-sampled cases into different strata. Hence, it is crucial to deal with these two goals simultaneously for the estimation of stratum-specific parameters. This study introduces the idea of a double-objective classification and regression trees (CARTs) approach for estimating stratum-specific parameters. Theoretical properties of the total estimator are derived. An application on the estimation of health outcomes in different domains is given to delineate the practical significance as well as the efficiency of the proposed CART-based method. The proposed estimator of population total performs better than the existing stratum-specific estimator in terms of relative efficiency for all choices of parameters. As an ensemble model, the random forest CART outperforms the other competing tree-based models and homogenous population model without using any auxiliary variable. [ABSTRACT FROM AUTHOR]

Background: Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA). Methods: The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group. Results: The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption. Conclusion: All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders. [ABSTRACT FROM AUTHOR]

Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this article, we aim to increase the awareness of statisticians to this relatively new inferential tool, and to facilitate its adoption by providing user-friendly open-source software. We also give attention to the ubiquitous problem of estimating parameters from replicated data, which we address using permutation-invariant neural networks. Through extensive simulation studies we demonstrate that neural Bayes estimators can be used to quickly estimate parameters in weakly identified and highly parameterized models with relative ease. We illustrate their applicability through an analysis of extreme sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates and bootstrap-based confidence intervals from hundreds of spatial fields in a fraction of a second. [ABSTRACT FROM AUTHOR]