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Syntheticdata generation methods are used to transform the original data into privacy-compliant synthetic copies (twin data). With our proposed approach, synthetic data can be simulated in the same size as the input data or in any size, and in the case of finite populations, even the entire population can be simulated. The proposed XGBoost-based method is compared with known model-based approaches to generate synthetic data using a complex survey data set. The XGBoost method shows strong performance, especially with synthetic categorical variables, and outperforms other tested methods. Furthermore, the structure and relationship between variables are well preserved. The tuning of the parameters is performed automatically by a modified k-fold cross-validation. If exact population margins are known, e.g., cross-tabulated population counts on age class, gender and region, the synthetic data must be calibrated to those known population margins. For this purpose, we have implemented a simulated annealing algorithm that is able to use multiple population margins simultaneously to post-calibrate a synthetic population. The algorithm is, thus, able to calibrate simulated population data containing cluster and individual information, e.g., about persons in households, at both person and household level. Furthermore, the algorithm is efficiently implemented so that the adjustment of populations with many millions or more persons is possible.

Der ehemalige TU Wien Professor Rudolf Dutter verstarb am 5. Mai 2023 an den Folgen seiner langjährigen Diabetes-Erkrankung. Prof.~Dutter war von 1997 bis 2003 Redakteur der Österreichischen Zeitschrift für Statistik (Austrian Journal of Statistics), und diese Tätigkeit hat er mit viel Engagement im Sinne der Österreichischen Statistischen Gesellschaft geleistet. Eine seiner Aktivitäten war die Einrichtung und der Betrieb einer Website für die Zeitschrift, die einen "Open Access" Zugriff auf die Artikel ermöglichte. Ein kurzer Nachruf in dieser Zeitschrift, auch als Information für die Mitglieder der Gesellschaft, scheint daher mehr als passend zu sein.

BackgroundData anonymization and sharing have become popular topics for individuals, organizations, and countries worldwide. Open-access sharing of anonymized data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. ObjectiveThis study aimed to highlight the requirements and possible solutions for sharing health surveillance event history data. The challenges lie in the anonymization of multiple event dates and time-varying variables. MethodsA sequential approach that adds noise to event dates is proposed. This approach maintains the event order and preserves the average time between events. In addition, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding the key variables that change over time, such as educational level or occupation, we make 2 proposals: one based on limiting the intermediate statuses of the individual and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga health and demographic surveillance system (HDSS) core residency data set, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 events with time-varying socioeconomic variables and demographic information. ResultsAn anonymized version of the event history data, including longitudinal information on individuals over time, with high data utility, was created. ConclusionsThe proposed anonymization of event history data comprising static and time-varying variables applied to HDSS data led to acceptable disclosure risk, preserved utility, and being sharable as public use data. It was found that high utility was achieved, even with the highest level of noise added to the core event dates. The details are important to ensure consistency or credibility. Importantly, the sequential noise addition approach presented in this study does not only maintain the event order recorded in the original data but also maintains the time between events. We proposed an approach that preserves the data utility well but limits the number of response categories for the time-varying variables. Furthermore, using distance-based neighborhood matching, we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers have full information on the original data. We showed that the disclosure risk is very low, even when assuming that the attacker’s database and information are optimal. The HDSS and medical science research communities in low- and middle-income country settings will be the primary beneficiaries of the results and methods presented in this paper; however, the results will be useful for anyone working on anonymizing longitudinal event history data with time-varying variables for the purposes of sharing.

Navigating the intricate world of data analytics, one method has emerged as a key tool in confronting missing data: multiple imputation. Its strength is further fortified by its powerful variant, robust imputation, which enhances the precision and reliability of its results. In the challenging landscape of data analysis, non-robust methods can be swayed by a few extreme outliers, leading to skewed imputations and biased estimates. This can apply to both representative outliers—those true yet unusual values of your population—and non-representative outliers, which are mere measurement errors. Detecting these outliers in large or high-dimensional data sets often becomes as complex as unraveling a Gordian knot. The solution? Turn to robust imputation methods. Robust (imputation) methods effectively manage outliers and exhibit remarkable resistance to their influence, providing a more reliable approach to dealing with missing data. Moreover, these robust methods offer flexibility, accommodating even if the imputation model used is not a perfect fit. They are akin to a well-designed buffer system, absorbing slight deviations without compromising overall stability. In the latest advancement of statistical methodology, a new robust imputation algorithm has been introduced. This innovative solution addresses three significant challenges with robustness. It utilizes robust bootstrapping to manage model uncertainty during the imputation of a random sample; it incorporates robust fitting to reinforce accuracy; and it takes into account imputation uncertainty in a resilient manner. Furthermore, any complex regression or classification model for any variable with missing data can be run through the algorithm. With this new algorithm, we move one step closer to optimizing the accuracy and reliability of handling missing data. Using a realistic data set and a simulation study including a sensitivity analysis, the new alogorithm imputeRobust shows excellent performance compared with other common methods. Effectiveness was demonstrated by measures of precision for the prediction error, the coverage rates, and the mean square errors of the estimators, as well as by visual comparisons.

The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria.

Aim: A growing number of studies call attention to the consequences of climate change on agriculture, including impacts of climate extremes such as drought, heat weaves and frost. The aim of this study was to quantify the influence of temperature extremes on various phenological events of grapevine varieties in south west Switzerland (Leytron, Canton of Valais). We aim to capture the occurrence of extreme events in specific years, grapevine varieties, and phenological phases to rank the varieties based on their sensitivity to temperature extremes and thus quantify their robustness. Methods and results: Phenological observations (1978 – 2018) for six varieties of Vitis vinifera ('Arvine', 'Chardonnay', 'Chasselas', 'Gamay', 'Pinot Noir', 'Syrah') were studied using event coincidence analysis. Extreme events were defined as values in the uppermost/lowermost percentiles of the distributions of the variables under study. The results showed that significantly more extreme temperature- and extreme phenological events occurred in Leytron between 2003 and 2017 than in earlier years. It was also found that the occurrence rate of extreme events was independent of the growth stage (between 13-19%). Based on the total number of extreme events the varieties do not differ in their response to temperature extremes. Conclusion: The event coincidence analysis is an appropriate tool to quantify the occurrence of extreme events. The occurrence of extremes temperature events on various grapevines had a clear effect in advancing the timing of phenological events. However, there was no varietal difference in the response of varieties to extreme temperature events, thus more research is needed to outline the best adaptation measures. These findings are remarkable in the era of climate change, when extreme climate events are predicted to occur more frequently. Significance of the study: Information on climate impacts is enabling appropriate adaptation responses and is supported with knowledge on how the different varieties are affected under extreme conditions. Our findings are important for Swiss winemakers when making decisions on vineyard management in the future.

A growing number of studies have highlighted the consequences of climate change on agriculture, including the impacts of climate extremes such as drought, heat waves and frost. The aim of this study was to assess the influence of temperature extremes on various phenological events of grapevine varieties in Southwest Switzerland (Leytron, Canton of Valais). We aimed to capture the occurrence of extreme events in specific years in various grapevine varieties and at different phenological phases to rank the varieties based on their sensitivity to temperature extremes and thus quantify their robustness. Phenological observations (1978–2018) of six Vitis vinifera varieties (Arvine, Chardonnay, Chasselas, Gamay, Pinot noir and Syrah) were subjected to event coincidence analysis. Extreme events were defined as values in the uppermost or lowermost percentiles of the timing of the phenophases and daily temperatures within a 30-day window before the phenophase event occurred. Significantly more extreme temperature and phenological events occurred in Leytron between 2003 and 2017 than in the earlier years, with the years 2007, 2011, 2014 and 2017 being remarkable in terms of the number of extreme coincidence events. Moreover, bud development and flowering experienced significantly more extreme coincidence events than other phenophases; however, the occurrence rate of extreme coincidence events was independent of the phenophase. Based on the total number of extreme events, the varieties did not differ in their responses to temperature extremes. Therefore, event coincidence analysis is an appropriate tool to quantify the occurrence of extreme events. The occurrence of extreme temperature events clearly affected the advancement of the timings of phenological events in various grapevines. However, there were no varietal differences in terms of response to extreme temperatures; thus, additional research is warranted to outline the best adaptation measures.

This article is motivated by the work as editor-in-chief of the Austrian Journal of Statistics and contains detailed analyses about the impact of the Austrian Journal of Statistics. The impact of a journal is typically expressed by journal metrics indicators. One of the important ones, the journal impact factor is calculated from the Web of Science (WoS) database by Clarivate Analytics. It is known that newly established journals or journals without membership in big publishers often face difficulties to be included, e.g., in the Science Citation Index (SCI) and thus they do not receive a WoS journal impact factor, as it is the case for example, for the Austrian Journal of Statistics. In this study, a novel approach is pursued modeling and predicting the WoS impact factor of journals using open access or partly open-access databases, like Google Scholar, ResearchGate, and Scopus. I hypothesize a functional linear dependency between citation counts in these databases and the journal impact factor. These functional relationships enable the development of a model that may allow estimating the impact factor for new, small, and independent journals not listed in SCI. However, only good results could be achieved with robust linear regression and well-chosen models. In addition, this study demonstrates that the WoS impact factor of SCI listed journals can be successfully estimated without using the Web of Science database and therefore the dependency of researchers and institutions to this popular database can be minimized. These results suggest that the statistical model developed here can be well applied to predict the WoS impact factor using alternative open-access databases.

For redistribution and operating bikes in a free-floating systems, two measures are of highest priority. First, the information about the expected number of rentals on a day is an important measure for service providers for management and service of their fleet. The estimation of the expected number of bookings is carried out with a simple model and a more complex model based on meterological information, as the number of loans depends strongly on the current and forecasted weather. Secondly, the knowledge of a service level violation in future on a fine spatial resolution is important for redistribution of bikes. With this information, the service provider can set reward zones where service level violations will occur in the near future. To forecast a service level violation on a fine geographical resolution the current distribution of bikes as well as the time and space information of past rentals has to be taken into account. A Markov Chain Model is formulated to integrate this information. We develop a management tool that describes in an explorative way important information about past, present and predicted future counts on rentals in time and space. It integrates all estimation procedures. The management tool is running in the browser and continuously updates the information and predictions since the bike distribution over the observed area is in continous flow as well as new data are generated continuously.

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.

The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.

This study presents an improvement to the mean-variance portfolio optimization model, by considering both the integer transaction lots and a robust estimator of the covariance matrices. Four robust estimators were tested, namely the Minimum Covariance Determinant, the S, the MM, and the Orthogonalized Gnanadesikan–Kettenring estimator. These integer optimization problems were solved using genetic algorithms. We introduce the lot turnover measure, a modified portfolio turnover, and the Robust Sharpe Ratio as the measure of portfolio performance. Based on the simulation studies and the empirical results, this study shows that the robust estimators outperform the classical MLE when data contain outliers and when the lots have moderate sizes, e.g. 500 shares or less per lot.

The package VIM (Templ, Alfons, Kowarik, and Prantner 2016) is developed to explore and analyze the structure of missing values in data using visualization methods, to impute these missing values with the built-in imputation methods and to verify the imputation process using visualization tools, as well as to produce high-quality graphics for publications. This article focuses on the different imputation techniques available in the package. Four different imputation methods are currently implemented in VIM, namely hot-deck imputation, k-nearest neighbor imputation, regression imputation and iterative robust model-based imputation (Templ, Kowarik, and Filzmoser 2011). All of these methods are implemented in a flexible manner with many options for customization. Furthermore in this article practical examples are provided to highlight the use of the implemented methods on real-world applications. In addition, the graphical user interface of VIM has been re-implemented from scratch resulting in the package VIMGUI (Schopfhauser, Templ, Alfons, Kowarik, and Prantner 2016) to enable users without extensive R skills to access these imputation and visualization methods.

The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data. The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.

The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. The original output from X-12-ARIMA is somehow static and it is not always an easy task for users to extract the required information for further processing. The R package x12 provides wrapper functions and an abstraction layer for batch processing of X-12-ARIMA. It allows summarizing, modifying and storing the output from X-12-ARIMA within a well-defined class-oriented implementation. On top of the class-oriented (command line) implementation the graphical user interface allows access to the R package x12 without requiring too much R knowledge. Users can interactively select additive outliers, level shifts and temporary changes and see the impact immediately. The provision of the powerful X-12-ARIMA seasonal adjustment program available directly from within R, as well as of the new facilities for marking outliers, batch processing and change tracking, makes the package a potent and functional tool.

The popularity of R is increasing in national statistical offices not only for simulation tasks. Nowadays R is also used in the production process. A lot of new features for various tasks in official statistics have been developed over the last years and these features are freely available in the form of add-on package. In this contribution we first give an outline of the use of R at Statistics Austria. Discussed is the necessary infrastructure according to the R-installation, the teaching of employees and the support provided to the staff who use R in their daily work. In the second part, the R developments from the methods unit at Statistics Austria are summarised. The developed packages include methods for data pre-processing (e.g imputation) up to packages for the final dissemination of data including packages for statistical disclosure control, estimation of indicators and the visualisation of results.

The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.

Units sampled from finite populations typically come with different inclusion proba- bilities. Together with additional preprocessing steps of the raw data, this yields unequal sampling weights of the observations. Whenever indicators are estimated from such com- plex samples, the corresponding sampling weights have to be taken into account. In addition, many indicators suffer from a strong influence of outliers, which are a common problem in real-world data. The R package laeken is an object-oriented toolkit for the estimation of indicators from complex survey samples via standard or robust methods. In particular the most widely used social exclusion and poverty indicators are imple- mented in the package. A general calibrated bootstrap method to estimate the variance of indicators for common survey designs is included as well. Furthermore, the package contains synthetically generated close-to-reality data for the European Union Statistics on Income and Living Conditions and the Structure of Earnings Survey, which are used in the code examples throughout the paper. Even though the paper is focused on showing the functionality of package laeken, it also provides a brief mathematical description of the implemented indicator methodology.

Dem ständigen wissenschaftlichen Fortschritt Rechnung tragend bietet Statistik Austria Interessierten die Möglichkeit, in methodischen Grundlagenkursen aktuelles statistisches Basiswissen zu erlangen, oder bereits erworbene Kenntnisse aufzufrischen und zu erweitern. Um mathematische Kursinhalte und komplexe Zusammenhänge möglichst kompakt und für alle verständlich zu transportieren, haben die Autoren des Artikels ein spezielles computergestütztes Statistiktraining entwickelt, welches seit 2005 im Rahmen hausinterner Mitarbeiterschulungen erfolgreich Anwendung findet. Das Lernsystem ist für die moderne computergestützte Lehre konzipiert und soll durch den hier präsentierten Einblick auch anderen Lehrenden oder Institutionen als alternative Idee oder mögliches Konzept einer effektiven Kursgestaltung vorgestellt werden.

Christian F. G. SCHENDERA (2010). Clusteranalyse mit SPSS. Mit Faktorenanalyse. Oldenbourg Verlag, München, 435 Seiten, ISBN 978-3-486-58691-6. (e49.80) Karlheinz ZWERENZ (2009). Statistik: Einführung in die computergestützte Datenanalyse (4. überarbeitete Auflage). Oldenbourg Verlag, München, 434 Seiten, ISBN 978- 3-486-59112-5. (e36.80)

The open-source programming language and software environment R is currently one of the most widely used and popular software tools for statistics and data analysis. This contribution provides an overview of important R packages used in official statistics and survey methodology and discusses the usefulness of R in the daily work of a statistical office. Examples of activities and developments in R related projects in several national and international statistical offices are given. The focus is not only on the internal infrastructure that national and international statistical offices provide for using R but also on some interesting R related projects carried out in those institutes. Two particular packages (laeken and sdcMicro) and one data set (Statistics on Earnings Survey) are used to illustrate the usefulness (and the user-friendliness) of R and to present methods available in R. In addition, the access to international statistical databases like WDI of World Bank and UN COMTRADE with R is illustrated.

Das Interview mit Ernst Stadlober wurde von Herwig Friedl und Matthias Templ am 18.12.2015 gefuhrt. Es zeichnet ein Bild des beruflichen Werdeganges von Ernst Stadlo- ¨ ber, von seinen Anf¨angen wo er mit fix gesetztem seed auf deterministischem Wege uber ¨ die Random Number Generation zu seiner sehr breiten Ausrichtung der Statistik fand. Viele erfolgreich angewandte Forschungsprojekte mit Partnern aus Verwaltung, Industrie und Wirtschaft bezeugen ebenso seine Erfolgsgeschichte als auch die beispiellose intensive Betreuung von Studenten an der TU Graz. Man kann zurecht behaupten, dass Ernst Stadlober ein breites Methodenspektrum aus dem Gebiet der Statistik beherrscht und es trotzdem schaffte in viele Spezialgebiete auch tiefer vorzudringen. Ernst Stadlobers berufliche Heimat war und ist das Statistikinstitut der TU Graz, das er seit 1998 auch leitet. Dazwischen war er auf Forschungsaufenthalten an der Stanford University/USA und der TH Darmstadt und hatte eine Lehrstuhlvertretung an der Universit¨at Kiel. Bis heute hat er 12 Dissertationen und mehr als 90 Diplom-/Masterarbeiten betreut. Zum Repertoire seiner Lehre z¨ahlt die (Angewandte) Statistik, Zeitreihenanalyse, Stochastische Modellierung und Simulation, Versuchsplanung und einiges mehr. Zus¨atzlich blickt er heute auf uber eine Reihe von ¨ 100 Vortr¨agen sowie auf etwa 80 Publikationen aus dem Bereich der Biostatistik, Computerstatistik und Angewandten Statistik zuruck.

In this contribution, indicators for computer-based analysis and assessment of tumor cell proliferation in human brain tumors are developed. The methods are applied on (two) samples of digitized human brain tumor tissue sections immunostained with an antibody against the Ki67 epitope. The Ki67 immunostaining highlights cells undergoing cell division and is thus a surrogate marker for tumor growth. The challenges are related to the enormous size of the images (“big data”) analyzed, some of them are larger than 100 GB. Thus, efficient methods to extract relevant information have to be applied. Before starting with the statistical analysis, the digitized images are preprocessed to extractthe highlighted cells. Then the distribution of Ki67 immunostaining patterns is analyzed. Starting with a bivariate kernel density estimation, the proposed indicators are used to evaluate and compare the resulting density estimates. Moreover, the spatial distribution of clusters of Ki67-labeled tumor cells is of particular interest. The results allow to evaluate and compare images or sectors of the images. This evaluation and comparisons of samples or sectors could turn out to be useful in practice since it allows for a pre-selection of interesting sectors and samples. Thus, the time-consuming part of manual inspection of the huge images could be reduced.

Visualising indicators in thematic maps is nowadays state-of-theart and many statistical agencies and data providers support their figures also within interactive visualisation. However, mostly raw data values are presented in maps and the visualisation of statistical estimation results is rarely done and topic in this contribution. For the estimation of cross-correlations of one reference time series to other time series, we show that it is important to prewhiten the time series based on the model estimates of the reference time series. In addition, a simple weighting of time series to increase the importance of recent years over values from the very past is proposed. Finally, an application of our implemented visualisation tool using European alcohol consumption statistics is shown.

Das Interview mit Peter Hackl wurde von Werner Muller und Matthias Templ am 20.03.2014 gehalten. Es zeichnet ein Bild des beruflichen Werdeganges von Peter Hackl, von der Physik und des Welthandeles mit der Statistik in der richtigen Skala, long runners bei Marathons und Textbuchern, von seiner Zeit an der Wirtschaftsuniversität in Wien, Abstechern zur "Handelshögskolan", nach Abu Dhabi und Peer-Reviews fur exotischen Institutionen wie Eurostat. Weiters werden seine zahlreichen Fuhrungsrollen in der ÖSG und in der Statistik Austria beleuchtet. Im Blickfeld ist auch eine Diskussion über die Ausrichtung der Statistik.

Das Interview mit Ewald Kutzenberger wurde von Werner Holzer und Matthias Templ am 22.10.2014 durchgeführt. Es beleuchtet den Werdegang von Ewald Kutzenberger, berichtet Hintergründe zur Entstehung des Bundesstatistikgesetzes und gibt Einblicke in den Übergang der Offiziellen Statistik in Österreich von der reinen Datensammlung zur methodischen Analyse von Daten und auch der Anknüpfung zu Universitäten. Unter anderem wird die politikberatende Funktion der Statistik in den Blickpunkt gerückt.

Das Interview mit Wilfried Grossmann wurde von Werner G. Müller und Matthias Templ am 17.2.2014 durchgeführt. Es beleuchtet das historische Klima des Statistikinstitutes an der Universität Wien, die Ausrichtung der Statistik als ”breite“ Datenwissenschaft and der Universität Wien, die Kooperation mit der TU Wien und anderen Institutionen, sowie das Verhältnis zur Statistik Austria, der Amtlichen Statistik und Universitätsstatistik. Zusätzlich wird die Rolle der ÖSG und von EUROSTAT beleuchted. Das Interview widmet sich ausserdem dem Studium der Statistik im Wandel der Zeit - von Lochkarten bis zur Softwareumgebung R und Big Data. Wilfried Grossmann ist Professor für Statistik am Institut für Scientific Computing und Forschungsgruppenleiter der Arbeitsgruppe Data Analysis and Computing an der Universität Wien. Er hat ca. 100 Forschungsarbeiten publiziert im Bereich Computational Statistics, Statistisches Datenmanagement, Angewandte Statistik, Theoretische Statistik und Operations Research. Seine aktuellen Forschungsinteressen gelten dem Statistischen Datenmanagement und Informationssystemen, Statistical Computing im Bereich der Offiziellen Statistik, Statistical Knowledge Management, Statistik in der Lehre und Informatik, analytische Methoden der Statistik and Anwendungen moderner Methoden des Data Mining.

The demand of data from surveys, registers or other data sets containing sensible information on people or enterprises have been increased significantly over the last years. However, before providing data to the public or to researchers, confidentiality has to be respected for any data set containing sensible individual information. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data. The research on SDC methods becomes more and more important in the last years because of an increase of the awareness on data privacy and because of the fact that more and more data are provided to the public or to researchers. However, for legal reasons this is only visible when the released data has (very) low disclosure risk. In this contribution existing disclosure risk methods are review and summarized. These methods are finally applied on a popular real-world data set - the Structural Earnings Survey (SES) of Austria. It is shown that the application of few selected anonymisation methods leads to well-protected anonymised data with high data utility and low information loss.

In order to facilitate teaching complex topics in an interactive way, the authors developed a computer-assisted teaching system, a graphical user interface named TGUI (Teaching Graphical User Interface). TGUI was introduced at the beginning of 2009 in the Austrian Journal of Statistics (Dinges and Templ 2009) as being an effective instrument to train and teach staff on mathematical and statistical topics. While the fundamental principles were retained, the current TGUI system has been undergone a complete redesign. The ultimate goal behind the reimplementation was to share the advantages of TGUI and provide teachers and people who need to hold training courses with a strong tool that can enrich their lectures with interactive features. The idea was to go a step beyond the current modular blended-learning systems (see, e.g., Da Rin 2003) or the related teaching techniques of classroom-voting (see, e.g., Cline 2006). In this paper the authors have attempted to exemplify basic idea and concept of TGUI by means of statistics seminars held at Statistics Austria. The powerful open source software R (R Development Core Team 2010a) is the backend for TGUI, which can therefore be used to process even complex statistical contents. However, with specifically created contents the interactive TGUI system can be used to support a wide range of courses and topics. The open source R packages TGUICore and TGUITeaching are freely available from the Comprehensive R Archive Network at http://CRAN.R-project.org/.

Simulation studies are widely used by statisticians to gain insight into the quality of developed methods. Usually some guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary in order to draw meaningful conclusions. The R package simFrame is an object-oriented framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with a minimal effort of programming. Its object-oriented implementation provides clear interfaces for extensions by the user. Since statistical simulation is an embarrassingly parallel process, the framework supports parallel computing to increase computational performance. Furthermore, an appropriate plot method is selected automatically depending on the structure of the simulation results. In this paper, the implementation of simFrame is discussed in great detail and the functionality of the framework is demonstrated in examples for different simulation designs.