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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 154)
Statistics in Medicine     Hybrid Journal   (Followers: 149)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Biometrics     Hybrid Journal   (Followers: 52)
Sociological Methods & Research     Hybrid Journal   (Followers: 45)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 30)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 24)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
International Statistical Review     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Argumentation et analyse du discours     Open Access   (Followers: 8)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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Similar Journals
Journal Cover
AStA Advances in Statistical Analysis
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1863-818X - ISSN (Online) 1863-8171
Published by Springer-Verlag Homepage  [2469 journals]
  • Sieve bootstrapping the memory parameter in long-range dependent
           stationary functional time series

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      Abstract: Abstract We consider a sieve bootstrap procedure to quantify the estimation uncertainty of long-memory parameters in stationary functional time series. We use a semiparametric local Whittle estimator to estimate the long-memory parameter. In the local Whittle estimator, discrete Fourier transform and periodogram are constructed from the first set of principal component scores via a functional principal component analysis. The sieve bootstrap procedure uses a general vector autoregressive representation of the estimated principal component scores. It generates bootstrap replicates that adequately mimic the dependence structure of the underlying stationary process. We first compute the estimated first set of principal component scores for each bootstrap replicate and then apply the semiparametric local Whittle estimator to estimate the memory parameter. By taking quantiles of the estimated memory parameters from these bootstrap replicates, we can nonparametrically construct confidence intervals of the long-memory parameter. As measured by coverage probability differences between the empirical and nominal coverage probabilities at three levels of significance, we demonstrate the advantage of using the sieve bootstrap compared to the asymptotic confidence intervals based on normality.
      PubDate: 2022-10-01
       
  • Correction to: Assessment of agricultural sustainability in European Union
           countries: a group-based multivariate trajectory approach

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      PubDate: 2022-09-01
       
  • Comment on: On the role of data, statistics and decisions in a pandemic
           statistics for climate protection and health—dare (more) progress!

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      Abstract: Abstract In the Corona pandemic, it became clear with burning clarity how much good quality statistics are needed, and at the same time how unsuccessful we are at providing such statistics despite the existing technical and methodological possibilities and diverse data sources. It is therefore more than overdue to get to the bottom of the causes of these issues and to learn from the findings. This defines a high aspiration, namely that firstly a diagnosis is carried out in which the causes of the deficiencies with their interactions are identified as broadly as possible. Secondly, such a broad diagnosis should result in a therapy that includes a coherent strategy that can be generalised, i.e. that goes beyond the Corona pandemic.
      PubDate: 2022-09-01
       
  • On the role of data, statistics and decisions in a pandemic

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      Abstract: Abstract A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic' How can the severity of the pandemic be measured' What is the effect of vaccination in the population and which groups should be vaccinated first' The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.
      PubDate: 2022-09-01
       
  • Ranked sparsity: a cogent regularization framework for selecting and
           estimating feature interactions and polynomials

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      Abstract: Abstract We explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in modeling applications when an expected disparity exists in the quality of information between different feature sets. Its presence can cause traditional and modern model selection methods to fail because such procedures commonly presume that each potential parameter is equally worthy of entering into the final model—we call this presumption “covariate equipoise.” However, this presumption does not always hold, especially in the presence of derived variables. For instance, when all possible interactions are considered as candidate predictors, the premise of covariate equipoise will often produce over-specified and opaque models. The sheer number of additional candidate variables grossly inflates the number of false discoveries in the interactions, resulting in unnecessarily complex and difficult-to-interpret models with many (truly spurious) interactions. We suggest a modeling strategy that requires a stronger level of evidence in order to allow certain variables (e.g., interactions) to be selected in the final model. This ranked sparsity paradigm can be implemented with the sparsity-ranked lasso (SRL). We compare the performance of SRL relative to competing methods in a series of simulation studies, showing that the SRL is a very attractive method because it is fast and accurate and produces more transparent models (with fewer false interactions). We illustrate its utility in an application to predict the survival of lung cancer patients using a set of gene expression measurements and clinical covariates, searching in particular for gene–environment interactions.
      PubDate: 2022-09-01
       
  • Improving the causal treatment effect estimation with propensity scores by
           the bootstrap

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      Abstract: Abstract When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.
      PubDate: 2022-09-01
       
  • A spatial randomness test based on the box-counting dimension

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      Abstract: Abstract Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log–log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.
      PubDate: 2022-09-01
       
  • Flexible models for non-equidispersed count data: comparative performance
           of parametric models to deal with underdispersion

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      Abstract: Abstract Count data as response variables are commonly modeled using Poisson regression models, which require equidispersion, i.e., equal mean and variance. However, this relationship does not always occur, and the variance may be higher or lower than the mean, phenomena are known as overdispersion and underdispersion, respectively. Non-equidispersion, when disregarded, can lead to a number of misinterpretations and inadequate predictions. Here, we compare the use of the COM-Poisson, double Poisson, Gamma-count, and restricted generalized Poisson models as a more flexible class for count problems associated with over- and underdispersion, since they have an additional parameter that allows more flexible analysis. The proposed method is useful in different applications, but here we provide an example using an underdispersed dataset concerning ecological invasion. For validation of the models, we use half-normal plots. The COM-Poisson, double Poisson, and Gamma-count performed best and properly modeled the underdispersion. The use of correct statistical models is recommended to handle this data property using objective criteria to ensure accurate statistical inferences.
      PubDate: 2022-09-01
       
  • Regional now- and forecasting for data reported with delay: toward
           surveillance of COVID-19 infections

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      Abstract: Abstract Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is, and to envision how the number of infections is going to evolve over the next days. However, as in many other situations involving compulsory registration of sensitive data, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.
      PubDate: 2022-09-01
       
  • Distributional properties of continuous time processes: from CIR to bates

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      Abstract: Abstract In this paper, we compute closed-form expressions of moments and comoments for the CIR process which allows us to provide a new construction of the transition probability density based on a moment argument that differs from the historic approach. For Bates’ model with stochastic volatility and jumps, we show that finite difference approximations of higher moments such as the skewness and the kurtosis are unstable and, as a remedy, provide exact analytic formulas for log-returns. Our approach does not assume a constant mean for log-price differentials but correctly incorporates volatility resulting from Ito’s lemma. We also provide R, MATLAB, and Mathematica modules with exact implementations of the theoretical conditional and unconditional moments. These modules should prove useful for empirical research.
      PubDate: 2022-08-25
       
  • Hierarchical disjoint principal component analysis

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      Abstract: Abstract Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest possible part of the total variance of the observed variables. Several methodologies have been proposed either to improve the interpretation of PCA results (e.g., by means of orthogonal, oblique rotations, shrinkage methods), or to model oblique components or factors with a hierarchical structure, such as in Bi-factor and High-Order Factor analyses. In this paper, we propose a new methodology, called Hierarchical Disjoint Principal Component Analysis (HierDPCA), that aims at building a hierarchy of disjoint principal components of maximum variance associated with disjoint groups of observed variables, from Q up to a unique, general one. HierDPCA also allows choosing the type of the relationship among disjoint principal components of two sequential levels, from the lowest upwards, by testing the component correlation per level and changing from a reflective to a formative approach when this correlation turns out to be not statistically significant. The methodology is formulated in a semi-parametric least-squares framework and a coordinate descent algorithm is proposed to estimate the model parameters. A simulation study and two real applications are illustrated to highlight the empirical properties of the proposed methodology.
      PubDate: 2022-08-24
       
  • Multiple imputation of ordinal missing not at random data

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      Abstract: Abstract We introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic which involved binary single-level and multilevel data to ordinal variables. We apply an ordered probit model with sample selection as base of our imputation algorithm. The applied model involves two equations that are modelled jointly where the first one describes the missing-data mechanism and the second one specifies the variable to be imputed. In addition, we develop a version for hierarchical data by incorporating random intercept terms in both equations. To fit this multilevel imputation model we use quadrature techniques. Two simulation studies validate the overall good performance of our single-level and multilevel imputation methods. In addition, we show its applicability to empirical data by applying it to a common research topic in educational science using data of the National Educational Panel Study (NEPS) and conducting a short sensitivity analysis. Our approach is designed to be used within the R software package mice which makes it easy to access and apply.
      PubDate: 2022-08-22
       
  • Testing for the presence of treatment effect under selection on
           observables

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      Abstract: Abstract The evaluation of the possible effects of a treatment on an outcome plays a central role in both theoretical and applied statistical and econometrical literature. This paper focuses on nonparametric tests for possible difference in the distribution of potential outcomes due to receiving or not receiving a treatment. The approach is based on weighting observed data on the basis on the estimated propensity score. Kolmogorov–Smirnov type and Wilcoxon–Mann–Whitney type tests are constructed, and their limiting distributions are studied. Rejection regions are obtained by inverting confidence intervals. This involves the study of appropriate estimators of the limiting variance of test statistics. Approximations of quantiles via subsampling are also considered. The merits of the different tests are studied by Monte Carlo simulation. An application to the construction of tests for stochastic dominance is provided.
      PubDate: 2022-08-09
       
  • Authors’ response: on the role of data, statistics and decisions in
           a pandemic

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      PubDate: 2022-07-30
       
  • A new price index for multi-period and multilateral comparisons

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      Abstract: Abstract Within the stochastic approach, this paper establishes a closed-form solution to the price index problem for an arbitrary number of periods or countries. The index’s reference basket merges the intersections of all couples of baskets in all periods/countries and provides an effective commodity coverage. Under spherical regression errors, the index satisfies the Geary–Khamis equation system and, as such, offers a general and compact representation of the latter as well as the inferential framework as a dowry. Furthermore, by relaxing sphericalness in favor of a more realistic assumption of commodity-dependent variances, a broader result is achieved. The solution to the price index problem thus obtained encompasses the Geary–Khamis formulation and sows the seeds to further advances.
      PubDate: 2022-07-12
      DOI: 10.1007/s10182-022-00457-5
       
  • Editorial special issue: Statistics in sports

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      Abstract: Abstract Triggered by advances in data gathering technologies, the use of statistical analyzes, predictions and modeling techniques in sports has gained a rapidly growing interest over the last decades. Today, professional sports teams have access to precise player positioning data and sports scientists design experiments involving non-standard data structures like movement-trajectories. This special issue on statistics in sports is dedicated to further foster the development of statistics and its applications in sports. The contributed articles address a wide range of statistical problems such as statistical methods for prediction of game outcomes, for prevention of sports injuries, for analyzing sports science data from movement laboratories, for measurement and evaluation of player performance, etc. Finally, also SARS-CoV-2 pandemic-related impacts on the sport’s framework are investigated.
      PubDate: 2022-07-11
      DOI: 10.1007/s10182-022-00453-9
       
  • Integration of model-based recursive partitioning with bias reduction
           estimation: a case study assessing the impact of Oliver’s four factors
           on the probability of winning a basketball game

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      Abstract: Abstract In this contribution, we investigate the importance of Oliver’s Four Factors, proposed in the literature to identify a basketball team’s strengths and weaknesses in terms of shooting, turnovers, rebounding and free throws, as success drivers of a basketball game. In order to investigate the role of each factor in the success of a team in a match, we applied the MOdel-Based recursive partitioning (MOB) algorithm to real data concerning 19,138 matches of 16 National Basketball Association (NBA) regular seasons (from 2004–2005 to 2019–2020). MOB, instead of fitting one global Generalized Linear Model (GLM) to all observations, partitions the observations according to selected partitioning variables and estimates several ad hoc local GLMs for subgroups of observations. The manuscript’s aim is twofold: (1) in order to deal with (quasi) separation problems leading to convergence problems in the numerical solution of Maximum Likelihood (ML) estimation in MOB, we propose a methodological extension of GLM-based recursive partitioning from standard ML estimation to bias-reduced (BR) estimation; and (2) we apply the BR-based GLM trees to basketball analytics. The results show models very easy to interpret that can provide useful support to coaching staff’s decisions.
      PubDate: 2022-07-04
      DOI: 10.1007/s10182-022-00456-6
       
  • Comment “On the role of data, statistics and decisions in a
           pandemic” by Jahn et al.

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      Abstract: Abstract We comment the paper by Jahn et al. (On the role of data, statistics and decisions in a pandemic, 2022).
      PubDate: 2022-06-18
      DOI: 10.1007/s10182-022-00451-x
       
  • Discussion on On the role of data, statistics and decisions in a pandemic

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      Abstract: Abstract The authors make an important contribution presenting a comprehensive and thoughtful overview about the many different aspects of data, statistics and data analyses in times of the recent COVID-19 pandemic discussing all relevant topics. The paper certainly provides a very valuable reflection of what has been done, what could have been done and what needs to be done. We contribute here with a few comments and some additional issues. We do not discuss all chapters of Jahn et al. (AStA Adv Stat Anal, 2022. 10.1007/s10182-022-00439-7), but focus on those where our personal views and experiences might add some additional aspects.
      PubDate: 2022-06-10
      DOI: 10.1007/s10182-022-00450-y
       
  • Describing a landscape we are yet discovering

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      PubDate: 2022-06-09
      DOI: 10.1007/s10182-022-00449-5
       
 
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