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Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Blinding is an established approach in clinical trials which aims to minimise the risk of performance and detection bias. There is little empirical evidence to guide UK clinical trials units (CTUs) about the practice of blinding statisticians. Guidelines recommend that statisticians remain blinded to allocation prior to the final analysis. As these guidelines are not based on empirical evidence, this study undertook a qualitative investigation relating to when and how statisticians should be blinded in clinical trials.Methods: Data were collected through online focus groups with various stakeholders who work in the delivery and oversight of clinical trials. Recordings of the focus groups were transcribed verbatim and thematic analysis was used to analyse the transcripts.Results: Thirty-seven participants from 19 CTUs participated in one of six focus groups. Four main themes were identified, namely statistical models of work, factors affecting the decision to blind statisticians, benefits of blinding/not blinding statisticians and practicalities. Factors influencing the decision to blind the statistician included available resources, study design and types of intervention and outcomes and analysis. Although blinding of the statistician is perceived as a desirable mitigation against bias, there was uncertainty about the extent to which an unblinded statistician might impart bias. Instead, in most cases, the insight that the statistician offers was deemed more important to delivery of a trial than the risk of bias they may introduce if unblinded. Blinding of statisticians was only considered achievable with the appropriate resource and staffing, which were not always available. In many cases, a standard approach to blinding was therefore considered unrealistic and impractical; hence the need for a proportionate risk assessment approach identifying possible mitigations.Conclusions: There was wide variation in practice between UK CTUs regarding the blinding of trial statisticians. A risk assessment approach would enable CTUs to identify risks associated with unblinded statisticians conducting the final analysis and alternative mitigation strategies. The findings of this study will be used to design guidance and a tool to support this risk assessment process. [ABSTRACT FROM AUTHOR]

Although researchers have valued dichotomization because it can simplify conducting and presenting analyses, statisticians have criticized the general use of this procedure because it reduces power and can lead to invalid results. Despite this, researchers continue to regularly dichotomize continuous measures, as evidenced by the presence of median splits and other forms of artificial categorization in the published literature. This suggests that researchers do not find the currently presented arguments against dichotomization compelling enough to avoid using the procedure. In response, this article provides a new, more pragmatic examination of the effects of dichotomization focused on how this practice can impede the pursuit of researchers' professional and personal goals. Specifically, we present an examination of the effects of dichotomization phrased in terms of the sensitivity, specificity, positive predictive value, and negative predictive value of hypothesis tests, showing that dichotomization makes it more likely researchers will publish false results and reduces their ability to detect true findings in their data. [ABSTRACT FROM AUTHOR]

Background: At the 2015 REWARD/EQUATOR conference on research waste, the late Doug Altman revealed that his only regret about his 1994 BMJ paper 'The scandal of poor medical research' was that he used the word 'poor' rather than 'bad'. But how much research is bad? And what would improve things?Main Text: We focus on randomised trials and look at scale, participants and cost. We randomly selected up to two quantitative intervention reviews published by all clinical Cochrane Review Groups between May 2020 and April 2021. Data including the risk of bias, number of participants, intervention type and country were extracted for all trials included in selected reviews. High risk of bias trials was classed as bad. The cost of high risk of bias trials was estimated using published estimates of trial cost per participant. We identified 96 reviews authored by 546 reviewers from 49 clinical Cochrane Review Groups that included 1659 trials done in 84 countries. Of the 1640 trials providing risk of bias information, 1013 (62%) were high risk of bias (bad), 494 (30%) unclear and 133 (8%) low risk of bias. Bad trials were spread across all clinical areas and all countries. Well over 220,000 participants (or 56% of all participants) were in bad trials. The low estimate of the cost of bad trials was £726 million; our high estimate was over £8 billion. We have five recommendations: trials should be neither funded (1) nor given ethical approval (2) unless they have a statistician and methodologist; trialists should use a risk of bias tool at design (3); more statisticians and methodologists should be trained and supported (4); there should be more funding into applied methodology research and infrastructure (5).Conclusions: Most randomised trials are bad and most trial participants will be in one. The research community has tolerated this for decades. This has to stop: we need to put rigour and methodology where it belongs - at the centre of our science. [ABSTRACT FROM AUTHOR]

The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students' drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students' images and students' demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research. [ABSTRACT FROM AUTHOR]

This work aimed at 100 MW's evaluation of the mechanical dynamic functional behavior in steam turbines of potency of electric generation, as from a model of multivariable statistical control of process ( Multivariable Statistical Control of Process - MSCP), that T2 of Hotelling uses the statistician 's like parameter symptom for the detection of the normal condition of the functioning of these turbines and the graphic of statistical control of process guy shewhart, for the statistician's monitoring T2 of Hotelling 's. A group model of parameters symptoms created of the turbine itself and the boss took like reference a sample of data representative of the population of gathered data, the standard normal condition of variability of the process represents which that they characterize this behavior. Methods were used as the mathematical and statistical modelation as from a model of statistical control of multivariable process. This model went validated as from a sample of data boss representative of the standard normal condition of variability of the process. [ABSTRACT FROM AUTHOR]

When New York City (NYC) became an epicenter of the COVID-19 pandemic in the spring of 2020, statistical consulting centers at academic medical institutions in the area were immediately inundated with requests from hospital leadership and researchers for methodological support to address different aspects of the outbreak. Statisticians suddenly had to pivot from their usual responsibilities to focus entirely on COVID-19 work, and consulting centers had to devise innovative strategies to restructure their workflow and develop new infrastructure to address the acute demand for support. As statisticians from seven NYC-area institutions, we share our experiences and lessons learned during the pandemic, with the hope that this will lead not only to better preparedness for future public health crises when the skills and expertise of statisticians are critically needed, but also to lasting improvements to the structure and practice of statistical consulting centers. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic cast a dramatic spotlight on the use of data as a fundamental component of good decision-making. Evaluating and comparing alternative policies required information on concurrent infection rates and insightful analysis to project them into the future. Statisticians in Israel were involved in these processes early in the pandemic in some silos as an ad-hoc unorganized effort. Informal discussions within the statistical community culminated in a roundtable, organized by three past presidents of the Israel Statistical Association, and hosted by the Samuel Neaman Institute in April 2021. The meeting was designed to provide a forum for exchange of views on the profession's role during the COVID-19 pandemic, and more generally, on its influence in promoting evidence-based public policy. This paper builds on the insights and discussions that emerged during the roundtable meeting and presents a general framework, with recommendations, for involving statisticians and statistics in decision-making. [ABSTRACT FROM AUTHOR]

When the design is cut in half and the aliasing formed, the new combined effect is HT ht Graph The aliased effect is the linear combination of the real effects in the system. Aliasing in a fractional-factorial design means that it is not possible to estimate all effects because the experimental matrix has fewer unique combinations than a full-factorial design. [Extracted from the article]

Standards, guidelines, and recommendations 3 in statistics are undoubtedly important building blocks of modern global official statistics. They are main achievements of the harmonization work of the global community of official statisticians and are the backbone of the system, essential for maintaining high quality by using well-developed and documented methodologies and procedures, and for facilitating cross-national and cross-regional comparison of scores on indicators and trends. However, beyond such positive assessed achievements, the development, implementation, and use of 'standards' can also be characterized by a variety of negatively assessed characteristics. For example, in the development of standards, not every situation of a country or region can be or is sufficiently weighted or involved, countries and regions lack resources for full implementation, and the use of the standards according to the 'required operational level' is insufficient or the 'standards' do not fit the socio-economic, cultural, or political situation in a country resulting in a non-valid picture and unfit for use in policymaking. Therefore, though the 'standards' are the backbone of modern official statistics it is valid to question if the frequent use of cross-national comparisons unequally dismisses the cultural specificities of a country or region. It is also relevant to ask if the standards that are used to produce the indicators for cross-national comparisons are sufficiently implemented to allow for valid comparisons. And in general, one might question if there is a misfit between the emphasis on and practice of cross-national comparisons by international organizations and the attention to the level and awareness of the implementation of the standards used to produce the indicators on the country level. [ABSTRACT FROM AUTHOR]

Use cases should be put in place to experiment with the latest privacy-preserving techniques for secure exchange of information on cross-border transactions and company structures of multinational enterprises for the consistent recording of information by countries on the role of multinational firms in national economies. The FOC Group launched regular consultations for two years at the global level with senior statisticians who were members of the Group, senior-level users, and the Chairs of the statistical groups established by the Statistical Commission and active in economic statistics. At its 50 HT ht session (in 2019), the United Nations Statistical Commission established the Friends of the Chair (FOC) Group on Economic Statistics with a two-year mandate till 2021 to organize consultations on the future of economic statistics with senior statisticians and economists of the global and regional statistical and user communities. [Extracted from the article]

Background: There is a global emphasis on expanding data collection for joint replacement procedures beyond implant attributes and progression to revision surgery. Patient-reported outcome measures (PROMs) are increasingly considered as an important measure of surgical outcomes from a patient's perspective. However, a major limitation preventing wider use of PROMs data in national data collection has been the inability to systematically collect and share electronic information with relevant stakeholders in a comprehensive and financially sustainable manner. Objective: This study reports on the development of an electronic data capture and reporting system by a national registry for the collection of PROMs and the processes used to identify and overcome barriers to implementation and uptake. The study also aims to provide a cost breakdown of establishing and maintaining a nationwide electronic PROMs program. Methods: Between 2018 and 2020, 3 governance and advisory committees were established to develop and implement a PROMs pilot program nested within a nationwide joint replacement registry. The program involved electronic collection of preoperative and 6-month postoperative data for hip, knee, or shoulder replacement surgery from 44 Australian hospitals. Resource requirements for the program included a project manager, software developers, data manager, and statistician. An online platform was tested, refined, and implemented for electronic PROMs collection with scalability considered for future expansion to all Australian hospitals and additional data fields. Technical capabilities included different access for multiple user types, patient registration, automatic reminders via SMS text messages and email, online consent, and patient outcome real-time dashboards accessible for different user groups (surgeons, patients, hospitals, and project stakeholders). Results: During the PROMs pilot period there were 19,699 primary procedures undertaken with 10,204 registered procedures in the electronic system. This equated to 51.80% of people who had a joint replacement at participating hospitals during this period. Patient registration and data collection were efficient (20-30 seconds and 10-12 minutes, respectively). Engagement with the reporting dashboards (as a proportion of those who viewed their dashboard) varied by user group: 197/277 (71.1%) hospital administrators, 68/129 (52.7%) project stakeholders, 177/391 (45.3%) surgeons, and 1138/8840 patients (12.9%). Cost analysis determined an overall cost per patient of Aus $7-15 (approximately US $5-12) for 2 PROMs collections per joint replacement procedure once the program was established. Conclusions: Successful implementation of an orthopedic PROMs program with planned scalability for a broader national rollout requires significant funding and staffing resources. However, this expenditure can be considered worthwhile, given that collection and reporting of PROMs can drive health care improvement processes. Further consideration of strategies to improve stakeholder engagement with electronic reporting dashboards (particularly for patients and surgeons) will be critical to the ongoing success of a national PROMs program. [ABSTRACT FROM AUTHOR]

From reading William John "Jack" Youden's books and articles, Youden (1900-1971), an analytical chemist, becomes an applied statistician by the time he joins the National Bureau of Standards (NBS) in 1948. This article traces his transition from chemist to applied statistician and what his body of work mostly at NBS (1948-1965) demonstrates about his practice of statistics and the role that applied statistical research plays in it. There is much we can learn from a master applied statistician. [ABSTRACT FROM AUTHOR]

We then express each Graph HT ht as a convex combination of the estimated vertices, with Graph HT ht being the resulting combination coefficient vector. (b) How to derive the limiting distribution of Graph HT ht , and (c) how to utilize such limiting distribution to improve community detection, diversity metric and other inference tasks? For (c), we completely agree that it is beneficial to account for the asymptotic behavior of Graph HT ht in estimation and inference. [Extracted from the article]

We congratulate the authors for their stimulating and thought-provoking work on network data analysis. In the article, the authors not only introduce a new large-scale and high-quality publication dataset that will surely become an important benchmark for further network research, but also present novel statistical methods and modeling which lead to very interesting findings about the statistics community. There is much material for thought and exploration. In this discussion, we will focus on the cocitation networks, and discuss a few points for the coauthorship networks toward the end. [ABSTRACT FROM AUTHOR]

We collected and cleaned a large dataset on publications in statistics. The dataset consists of the co-author relationships and citation relationships of 83, 331 articles published in 36 representative journals in statistics, probability, and machine learning, spanning 41 years. The dataset allows us to construct many different networks, and motivates a number of research problems about the research patterns and trends, research impacts, and network topology of the statistics community. In this article we focus on (i) using the citation relationships to estimate the research interests of authors, and (ii) using the co-author relationships to study the network topology. Using co-citation networks we constructed, we discover a "statistics triangle," reminiscent of the statistical philosophy triangle (Efron 1998). We propose new approaches to constructing the "research map" of statisticians, as well as the "research trajectory" for a given author to visualize his/her research interest evolvement. Using co-authorship networks we constructed, we discover a multi-layer community tree and produce a Sankey diagram to visualize the author migrations in different sub-areas. We also propose several new metrics for research diversity of individual authors. We find that "Bayes," "Biostatistics," and "Nonparametric" are three primary areas in statistics. We also identify 15 sub-areas, each of which can be viewed as a weighted average of the primary areas, and identify several underlying reasons for the formation of co-authorship communities. We also find that the research interests of statisticians have evolved significantly in the 41-year time window we studied: some areas (e.g., biostatistics, high-dimensional data analysis, etc.) have become increasingly more popular. The research diversity of statisticians may be lower than we might have expected. For example, for the personalized networks of most authors, the p-values of the proposed significance tests are relatively large. [ABSTRACT FROM AUTHOR]

Although Bayesian inference is a popular paradigm among a large segment of scientists, including statisticians, most applications consider objective priors and need critical investigations. While it has several optimal properties, Bayesian inference lacks robustness against data contamination and model misspecification, which becomes a problem when using objective priors. As such, we present a general formulation of a Bayes pseudo-posterior distribution that leads to robust inference. Exponential convergence results related to the new pseudo-posterior and the corresponding Bayes estimators are established under a general parametric setup, and illustrations are provided for independent stationary and nonhomogeneous models. Several additional details and properties of the procedure are described, including estimation under fixed-design regression models. [ABSTRACT FROM AUTHOR]

While the threat of biased AI has received considerable attention, another invisible threat to data democracy exists that has not received scientific or media attention. This threat is the lack of autonomy for the 13 principal United Statesfederal statistical agencies. These agencies collect data that informs the United States federal government’s critical decisions, such as allocating resources and providing essential services. The lack of agency-specific statutory autonomy protections leaves the agencies vulnerable to political influence, which could have lasting ramifications without the public’s knowledge. Citro et al. evaluate the professional autonomy of the 13 federal statistical agencies and found that they lacked sufficient autonomy due to the absence of statutory protections (among other things). They provided three recommendations to enhance the strength of the federal statistical agency’s leadership and its autonomy to address each measure of autonomy for all 13 principal federal statistical agencies. Implementing these recommendations is an initial and crucial step toward preventing future erosion of the federal statistical system. Further, statisticians must take an active role in initiating and engaging in open dialogues with various scientific fields to protect and promote the vital work of federal statistical agencies. [ABSTRACT FROM AUTHOR]

The recent rapid spread of deadly epidemics have precipitated an urgent need to speed up the development of different treatments, as well as methods of evaluating their efficacy. The first step towards this is the collection of data relating to the cure rate in groups of patients who have had different treatments applied to them. As most of the available data in these cases is random, it is now the role of statisticians to provide efficient statistical tests to assess the treatment methods through the data. This research aims to provide a new statistical test with high efficiency to reach the right decision with accurate results as quickly as possible using parallel computing algorithms through Wolfram Mathematica software. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimation of population mean of the study character in two-occasion successive sampling in presence of non-response and measurement error. A logarithmic type imputation technique has been developed to reduce the nuisance effect of non-response in sample surveys. Utilizing information available on a highly positively or highly negatively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. An optimal replacement policy has been derived, and effect of measurement errors on the mean square error of the estimator has been studied. Empirical studies have been carried out using both real and simulated data, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems. [ABSTRACT FROM AUTHOR]

A statistician having collected and analyzed data has to draw inferences and explain their significance. In interpretation, the researcher goes beyond the descriptive data to extract meaning and insights from the data. Various methods are applied in the collection and analysis of statistical data. Statistics are not an end in themselves but they are a means to an end, the end being to draw certain conclusions from them. This has to be done very carefully, otherwise, misleading conclusions may be drawn and the whole purpose of doing research may get vitiated. A researcher or statistician besides the collection and analysis of data has to draw inferences and explain their significance. Through interpretation the meanings and implications of the study become clear. An analysis is not complete without interpretation, and interpretation cannot proceed without analysis. Both are, thus, interdependent. This paper attempts to articulate some basic steps and processes involved in the interpretation of Statistical data. [ABSTRACT FROM AUTHOR]

Analysing data from experiments is a complex, multi‐step process, often with multiple defensible choices available at each step. While analysts often report a single analysis without documenting how it was chosen, this can cause serious transparency and methodological issues. To make the sensitivity of analysis results to analytical choices transparent, some statisticians and methodologists advocate the use of 'multiverse analysis': reporting the full range of outcomes that result from all combinations of defensible analytic choices. Summarizing this combinatorial explosion of statistical results presents unique challenges; several approaches to visualizing the output of multiverse analyses have been proposed across a variety of fields (e.g. psychology, statistics, economics, neuroscience). In this article, we (1) introduce a consistent conceptual framework and terminology for multiverse analyses that can be applied across fields; (2) identify the tasks researchers try to accomplish when visualizing multiverse analyses and (3) classify multiverse visualizations into 'archetypes', assessing how well each archetype supports each task. Our work sets a foundation for subsequent research on developing visualization tools and techniques to support multiverse analysis and its reporting. [ABSTRACT FROM AUTHOR]

This article covers a debate between leading GDR statisticians, economists, and the party apparatus that took place during the so-called revisionism campaign against the Marxist intelligentsia. At stake was the socialist critique of statistics and its use in political economy. The immediate occasion of the debate was a doctoral thesis written by Ernst Strnad, unsolicited without supervision but drawing the attention of high party officials. Representative of the power play between academia and party, the case of Strnad shows that in spite of the increased party influence, universities were not willing to give up their academic ethos and maintained the basic mechanism of academic inclusion and exclusion. [ABSTRACT FROM AUTHOR]

Background: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards.Methods: We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards.Results: We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible.Conclusions: The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test. [ABSTRACT FROM AUTHOR]

As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians' toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using "black-boxes" and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, we apply and extend Valcour's model regarding the work-life balance of statisticians in Greece. This is a pilot study which examines the overall level of satisfaction regarding the meeting of work and family or personal role demand. Design/Methodology/Approach: We use survey data, collected from a questionnaire addressed to graduates from the Statistics Department of the Athens University of Economics and Business. Hierarchical Regression model and the Generalized Multiple Linear Regression model (GLM) are used to estimate an extended version of Valcour's model(2007). Findings: Results show a negative relationship between satisfaction (from balancing work and personal or family life) and total working hours, commute time, neuroticism and number of children. At the same time, the use of the GLM model strengthens Valcour's model by receiving additional information about the magnitude of the impact of changes in demographic and social factors on satisfaction. Practical Implications: The results regarding the work -life balance has managerial implcations. An organization which takes into account these findings provides a good work environment and facilities increasing the motivation of the employees. [ABSTRACT FROM AUTHOR]

The indicator variable, Graph, takes the value 1 when life Graph has risk factor, Graph and zero otherwise. To put it more formally, we could say that the estimation risk is the uncertainty over, Graph whereas mis-estimation risk is the uncertainty over, Graph where Graph is our liability function. According to the maximum likelihood theorem, Graph has an approximate multivariate normal distribution with mean vector of Graph and covariance matrix Graph, which is estimated as the negative inverse of the Hessian. This means that if Graph has a multivariate normal distribution, all of our estimation risk is summarised in Graph In particular, the leading diagonal of Graph is the variance of the individual components of Graph and all the off-diagonal entries of Graph contain the covariances of the various components of Graph with each other. [Extracted from the article]

Copyright of Sociology: Theory, Methods, Marketing is the property of Institute of Sociology NAS of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In research, policy, and practice, continuous variables are often categorized. Statisticians have generally advised against categorization for many reasons, such as loss of information and precision as well as distortion of estimated statistics. Here, a different kind of problem with categorization is considered: the idea that, for a given continuous variable, there is a unique set of cut points that is the objectively correct or best categorization. It is shown that this is unlikely to be the case because categorized variables typically exist in webs of statistical relationships with other variables. The choice of cut points for a categorized variable can influence the values of many statistics relating that variable to others. This essay explores the substantive trade‐offs that can arise between different possible cut points to categorize a continuous variable, making it difficult to say that any particular categorization is objectively best. Limitations of different approaches to selecting cut points are discussed. Contextual trade‐offs may often be an argument against categorization. At the very least, such trade‐offs mean that research inferences, or decisions about policy or practice, that involve categorized variables should be framed and acted upon with flexibility and humility. Lay Summary: In research, policy, and practice, continuous variables are often turned into categorical variables with cut points that define the boundaries between categories. This involves choices about how many categories to create and what cut‐point values to use.This commentary shows that different choices about which cut points to use can lead to different sets of trade‐offs across multiple statistical relationships between the categorized variable and other variables.These trade‐offs mean that no single categorization is objectively best or correct. This context is critical when one is deciding whether and how to categorize a continuous variable. In practical settings, the choice of cut points for categorizing a continuous variable is likely to entail trade‐offs across multiple statistical relationships between the categorized variable and other variables. These trade‐offs mean that no single categorization is objectively best or correct. [ABSTRACT FROM AUTHOR]

Summary: Beta regression has been extensively used by statisticians and practitioners to model bounded continuous data without a strong competitor having the same main features. A class of normalised inverse‐Gaussian (N‐IG) process was introduced in the literature and has been explored in the Bayesian context as a powerful alternative to the Dirichlet process. Until this moment, no attention has been paid to the univariate N‐IG distribution in the classical inference. In this paper, we propose the bessel regression based on the univariate N‐IG distribution, which is an alternative to the beta model. The estimation of the parameters is done through an expectation–maximisation (EM) algorithm and the paper discusses how to perform inference. A useful and practical discrimination procedure is proposed for model selection between bessel and beta regressions. A new R package called bbreg is developed for fitting both bessel and beta regression models based on the EM‐algorithm and further providing graphical tools for model adequacy and model selection as well. Proper documentation for this package is available. The performances of the models are evaluated under misspecification in a simulation study. An empirical illustration is explored to confront results from bessel and beta regressions by using the new R package bbreg. [ABSTRACT FROM AUTHOR]

Argument: This paper examines self-inscription, a mode of census enumeration that emerged during the nineteenth century. Starting in the 1840s, a number of European states introduced self-inscription as an auxiliary means to facilitate the work of enumerators. However, a decisive shift occurred when Prussian census statisticians implemented self-inscription via individual "Zählkarten"—or "counting cards"—in 1871. The paper argues that scientific ideals of accuracy and precision prevalent in the sciences at the time motivated Prussian census officials to initiate self-inscription as an at-home scenario unmediated by enumerators, in which the census form alone was to yield truthful information from the respondents. By illuminating the bureaucratic means for implementing scientific ideals and practices in gathering personal census data, the paper offers an in-depth analysis of the media, technologies, and manpower that census takers deployed to reveal the epistemic—as well as social and political—impact of being "true to form." [ABSTRACT FROM AUTHOR]

Aim: This article covers an important subject - the assessment of the educational potential of the regional population of the Russian Federation. This is one of the most fundamental issues in modern society to be considered for the future development of Russia. The authors look at the concept of educational potential, systematize its key indicators, and perform an index analysis of factors that characterize educational activity and the technical, material, and informational basis of education in the Russian regions. Methodology: This article introduces a number of integrated components that characterize educational activities as well as the technical, material, and informational basis of education. A structured list of recommendations is provided for the implementation of multidimensional regional classification, in line with both individual as well as broad indicators covered in the analysis. Results: The regression model is based on an integral indicator that characterizes educational activity and defines the index of educational performance: the share of employed, higher-educated members of the population aged 25-64 years in the total number of economically active people of the same age group. Integral indicators characterizing educational activity and the technical, material, and informational basis of education were used in the benchmark analysis which defined leading and lagging regions in the Russian Federation. Based on these indicators, a multidimensional breakdown structure was developed to distinguish the weaknesses of other regions in relation to the leader. Conclusion: These results may be useful for statisticians and economists for research on the level of informatization of the population and the economy. [ABSTRACT FROM AUTHOR]

This study summarises the knowledge on the number of Trianon refugees. The study argues that one do not and will not have the opportunity to determine the number of refugees with approximate accuracy. The figure of 350,000 people published by the National Office for Refugees is only a partial result and the situation is complicated by the fact that it cannot be verified due to the disappearance of official documents. The reliability of the data has already been criticised by prominent statisticians of the Interwar Period and there have been several attempts to calculate using census data. However, not only were these materials unsuitable for establishing the exact number due to their staticity, but the data recordings for 1930 are no longer available in the archives. This study summarises the knowledge on the number of Trianon refugees. The study argues that one do not and will not have the opportunity to determine the number of refugees with approximate accuracy. The figure of 350,000 people published by the National Office for Refugees is only a partial result and the situation is complicated by the fact that it cannot be verified due to the disappearance of official documents. The reliability of the data has already been criticised by prominent statisticians of the Interwar Period and there have been several attempts to calculate using census data. However, not only were these materials unsuitable for establishing the exact number due to their staticity, but the data recordings for 1930 are no longer available in the archives. [ABSTRACT FROM AUTHOR]

Heavy tailed distributions have a big role in studying risk data sets. Statisticians in many cases search try to find new or relatively new statistical models to fit data sets in different fields. This article introduced a relatively new heavy-tailed statistical model by using alpha power transformation and exponentiated log-logistic distribution, which called alpha power exponentiated log-logistic distribution. Its statistical properties were derived mathematically such as moments, moment generating function, quantile function, entropy, inequality curves and order statistics. Six estimation methods were introduced mathematically and the behaviour of the proposed model parameters was checked by randomly generated data sets and these estimation methods. Also, some actuarial measures were deduced mathematically such as value at risk, tail value at risk, tail variance and tail variance premium. Numerical values for these measures were performed and proved that the proposed distribution has a heavier tail than others compared models. Finally, three real data sets from different fields were used to show how these proposed models fitting these data sets than other many wells known and related models. [ABSTRACT FROM AUTHOR]

In public health literature, the risk of death or disease associated with genetic, dietary, environmental, or behavioral factors is most commonly denoted by the odds ratio (OR), hazard ratio (HR), or risk ratio (RR). But how do people intuitively perceive risk? We conducted a small experiment in which respondents of different ages, sex, and education were asked to rank the risks associated with four different habits based on the incidence data of an imaginary deadly disease. Results showed that people judge risk by probability difference rather than probability ratio. Even individuals formally trained to use OR and HR as risk indicators preferred using probability differences over ratios to judge their own risk. This preliminary inquiry into intuitive statistical perception suggests that designing statistical indices based on people's innate perception may be a better strategy than trying to educate people to understand the indices designed by expert statisticians. [ABSTRACT FROM AUTHOR]

Hamiltonian Monte Carlo (HMC) is a powerful tool for Bayesian computation. In comparison with the traditional Metropolis–Hastings algorithm, HMC offers greater computational efficiency, especially in higher dimensional or more complex modeling situations. To most statisticians, however, the idea of HMC comes from a less familiar origin, one that is based on the theory of classical mechanics. Its implementation, either through Stan or one of its derivative programs, can appear opaque to beginners. A lack of understanding of the inner working of HMC, in our opinion, has hindered its application to a broader range of statistical problems. In this article, we review the basic concepts of HMC in a language that is more familiar to statisticians, and we describe an HMC implementation in R, one of the most frequently used statistical software environments. We also present hmclearn, an R package for learning HMC. This package contains a general-purpose HMC function for data analysis. We illustrate the use of this package in common statistical models. In doing so, we hope to promote this powerful computational tool for wider use. Example code for common statistical models is presented as for online publication. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimation of population variance of the study character in stratified successive sampling over two occasions under random non-response. Utilizing information available on a highly positively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. Calibration techniques have been applied for determining the optimum strata weight. Empirical studies have been carried out using both real and simulated data to show the efficiency of our proposed estimator over standard estimator, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems. [ABSTRACT FROM AUTHOR]

It is not unusual for a data analyst to encounter datasets distributed across several computers. This can happen for reasons such as privacy concerns, efficiency of likelihood evaluations, or just the sheer size of the whole dataset. This presents new challenges to statisticians as even computing simple summary statistics such as the median becomes computationally challenging. Furthermore, if other advanced statistical methods are desired, then novel computational strategies are needed. In this article, we propose a new approach for distributed analysis of massive data that is suitable for generalized fiducial inference and is based on a careful implementation of a "divide-and-conquer" strategy combined with importance sampling. The proposed approach requires only small amount of communication between nodes, and is shown to be asymptotically equivalent to using the whole dataset. Unlike most existing methods, the proposed approach produces uncertainty measures (such as confidence intervals) in addition to point estimates for parameters of interest. The proposed approach is also applied to the analysis of a large set of solar images. for this article are available online. [ABSTRACT FROM AUTHOR]

One of the multiple decisions that statisticians have to face on the release of seasonal and calendar adjusted series, is the revision policy when new data are available. The INE used to apply the policy of Partial Concurrent Adjustment: ARIMA Parameters in JDemetra + , but huge revisions from the beginning of the series were occasionally observed. Analyzing this issue deeply, we realized that revisions were due to two main reasons: model changes because of lack of admissible decomposition, and especially, changes in the autoregressive roots assignment. In this paper, we present the new revision policy applied at the INE, which may be considered a compromise between the Partial Concurrent Adjustment: ARIMA Parameters policy and the Partial Concurrent Adjustment: Fixed Model, both implemented in JDemetra +. This new policy avoids model changes by: (i) fixing the last estimated model with admissible decomposition when a model change is triggered and (ii) adjusting root assignment parameters to make sure autoregressive roots remain in the same component. In doing so, we improve the estimation of the model parameters with the new data, while avoiding big revisions, as shown in the examples. [ABSTRACT FROM AUTHOR]

For decades, statisticians and methodologists have insisted researchers utilize graphical analysis much more heavily. Despite cogent and passionate recommendations, there has been no graphical revolution. Instead, researchers rely heavily on misleading graphics that violate visual processing heuristics. Perhaps the main reason for the persistence of deceptive graphics is software; most software familiar to psychological researchers suffer from poor defaults and limited capabilities. Also, visualization is ancillary to statistical analysis, providing an incentive to not produce graphics at all. In this paper, we argue that every statistical analysis must have an accompanying graphic, and we introduce the point-and-click software Flexplot, available both in JASP and Jamovi. We then present the theoretical framework that guides Flexplot, as well as show how to perform the most common statistical analyses in psychological literature. [ABSTRACT FROM AUTHOR]

A recent trend in medical research is to develop prediction models aiming to improve patient care and health outcomes. While statisticians and data scientists are well-trained in the methods and process of developing a prediction model, their role post-model-development is less clear. This paper covers the critical scientific reasoning step in the prediction pipeline after a model is developed. Working collaboratively with domain experts, statisticians and data scientists should critically evaluate models, carefully implement models into practice, and assess the model's impact in real world settings. Constructs from implementation science are discussed in the context of prediction modeling. The paper focuses on clinical prediction models, but these ideas apply to other domains as well. [ABSTRACT FROM AUTHOR]