A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

              [Sort by number of followers]   [Restore default list]

  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 37)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 38)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 8)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 19)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 11)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Probability and Statistics     Open Access   (Followers: 11)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 281)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 144)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

              [Sort by number of followers]   [Restore default list]

Similar Journals
Journal Cover
TEST
Journal Prestige (SJR): 1.514
Citation Impact (citeScore): 1
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1863-8260 - ISSN (Online) 1133-0686
Published by Springer-Verlag Homepage  [2468 journals]
  • Comments on: Data integration via analysis of subspaces (DIVAS)

    • Free pre-print version: Loading...

      PubDate: 2024-06-06
       
  • Comments on: Data integration via analysis of subspaces (DIVAS)

    • Free pre-print version: Loading...

      PubDate: 2024-06-04
       
  • A new sufficient dimension reduction method via rank divergence

    • Free pre-print version: Loading...

      Abstract: Abstract Sufficient dimension reduction is commonly performed to achieve data reduction and help data visualization. Its main goal is to identify functions of the predictors that are smaller in number than the predictors and contain the same information as the predictors for the response. In this paper, we are concerned with the linear functions of the predictors, which determine a central subspace that preserves sufficient information about the conditional distribution of a response given covariates. Many methods have been developed in the literature for the estimation of the central subspace. However, most of the existing sufficient dimension reduction methods are sensitive to outliers and require some strict restrictions on both covariates and response. To address this, we propose a novel dependence measure, rank divergence, and develop a rank divergence-based sufficient dimension reduction approach. The new method only requires some mild conditions on the covariates and response and is robust to outliers or heavy-tailed distributions. Moreover, it applies to both discrete or categorical covariates and multivariate responses. The consistency of the resulting estimator of the central subspace is established, and numerical studies suggest that it works well in practical situations.
      PubDate: 2024-05-30
       
  • Partly linear instrumental variables regressions without smoothing on the
           instruments

    • Free pre-print version: Loading...

      Abstract: Abstract We consider a semiparametric partly linear model identified by instrumental variables. We propose an estimation method that does not smooth on the instruments and we extend the Landweber–Fridman regularization scheme to the estimation of this semiparametric model. We then show the asymptotic normality of the parametric estimator and obtain the convergence rate for the nonparametric estimator. Our estimator that does not smooth on the instruments coincides with a typical estimator that does smooth on the instruments but keeps the respective bandwidth fixed as the sample size increases. We propose a data driven method for the selection of the regularization parameter, and in a simulation study we show the attractive performance of our estimators.
      PubDate: 2024-05-30
       
  • Asymptotic results for nonparametric regression estimators after
           sufficient dimension reduction estimation

    • Free pre-print version: Loading...

      Abstract: Abstract Prediction, in regression and classification, is one of the main aims in modern data science. When the number of predictors is large, a common first step is to reduce the dimension of the data. Sufficient dimension reduction (SDR) is a well-established paradigm of reduction that keeps all the relevant information in the covariates X that is necessary for the prediction of Y. In practice, SDR has been successfully used as an exploratory tool for modeling after estimation of the sufficient reduction. Nevertheless, even if the estimated reduction is a consistent estimator of the population, there is no theory supporting this step when nonparametric regression is used in the imputed estimator. In this paper, we show that the asymptotic distribution of the nonparametric regression estimator remains unchanged whether the true SDR or its estimator is used. This result allows making inferences, for example, computing confidence intervals for the regression function, thereby avoiding the curse of dimensionality.
      PubDate: 2024-05-28
       
  • Local influence analysis in the softplus INGARCH model

    • Free pre-print version: Loading...

      Abstract: Abstract In statistical diagnostics, detecting influential observations is pivotal for assessing model fitting. To address parameter restrictions while maintaining necessary properties, the softplus INGARCH model has emerged as an alternative to the INGARCH model and its variants. This paper delves into statistical diagnostics within the softplus INGARCH model using local influence analysis, establishing a framework encompassing first-order diagnostics, second-order diagnostics and stepwise diagnostics. Additionally, we focus on perturbation schemes, refining conventional approaches and offering modifications. To demonstrate the effectiveness and suitability of our proposed methodology, particularly with the inclusion of stepwise diagnostics, we analyze two simulated datasets and two real-world examples. Compared to traditional methods, our approach adeptly handles potential issues such as the “masking effect” and “smearing effect” without necessitating complex calculations.
      PubDate: 2024-05-22
       
  • Correction to: A general near-exact distribution theory for the most
           common likelihood ratio test statistics used in Multivariate Analysis

    • Free pre-print version: Loading...

      PubDate: 2024-05-02
       
  • Privacy-preserving parametric inference for spatial autoregressive model

    • Free pre-print version: Loading...

      Abstract: Abstract Spatial regression models are important tools in dealing with spatially dependent data and are widely used in many fields such as spatial econometric and regional science. When the spatial data contain sensitive information, the privacy of the data will be compromised along with the release of the analysis if appropriate privacy-preserving measures are not in place. In this paper, we study the privacy-preserving parametric inference for the spatial autoregressive model and propose corresponding differentially private algorithm. We construct a differentially private spatial autoregression framework that takes graph data into account. We improve the functional mechanism to be more accurate under the same degree of privacy protection. Theoretical analysis establishes both the privacy guarantees of the algorithm and the asymptotic normality of the estimation. Simulation and real data studies show improvements of our approach.
      PubDate: 2024-04-09
       
  • Multiple change point detection for high-dimensional data

    • Free pre-print version: Loading...

      Abstract: Abstract This research investigates the detection of multiple change points in high-dimensional data without particular sparse or dense structure, where the dimension can be of exponential order in relation to the sample size. The estimation approach proposed employs a signal statistic based on a sequence of signal screening-based local U-statistics. This technique avoids costly computations that exhaustive search algorithms require and mitigates false positives, which hypothesis testing-based methods need to control. Consistency of estimation can be achieved for both the locations and number of change points, even when the number of change points diverges at a certain rate as the sample size increases. Additionally, the visualization nature of the proposed approach makes plotting the signal statistic a useful tool to identify locations of change points, which distinguishes it from existing methods in the literature. Numerical studies are performed to evaluate the effectiveness of the proposed technique in finite sample scenarios, and a real data analysis is presented to illustrate its application.
      PubDate: 2024-03-25
      DOI: 10.1007/s11749-024-00926-w
       
  • Data integration via analysis of subspaces (DIVAS)

    • Free pre-print version: Loading...

      Abstract: Abstract Modern data collection in many data paradigms, including bioinformatics, often incorporates multiple traits derived from different data types (i.e., platforms). We call this data multi-block, multi-view, or multi-omics data. The emergent field of data integration develops and applies new methods for studying multi-block data and identifying how different data types relate and differ. One major frontier in contemporary data integration research is methodology that can identify partially shared structure between sub-collections of data types. This work presents a new approach: Data Integration Via Analysis of Subspaces (DIVAS). DIVAS combines new insights in angular subspace perturbation theory with recent developments in matrix signal processing and convex–concave optimization into one algorithm for exploring partially shared structure. Based on principal angles between subspaces, DIVAS provides built-in inference on the results of the analysis, and is effective even in high-dimension-low-sample-size (HDLSS) situations.
      PubDate: 2024-03-14
      DOI: 10.1007/s11749-024-00923-z
       
  • Publisher Correction: Unit-Weibull autoregressive moving average models

    • Free pre-print version: Loading...

      PubDate: 2024-03-01
      DOI: 10.1007/s11749-023-00905-7
       
  • Unit-Weibull autoregressive moving average models

    • Free pre-print version: Loading...

      Abstract: Abstract In this work we introduce the class of Unit-Weibull Autoregressive Moving Average models for continuous random variables taking values in (0, 1). The proposed model is an observation driven one, for which, conditionally on a set of covariates and the process’ history, the random component is assumed to follow a Unit-Weibull distribution parameterized through its \(\rho \) th quantile. The systematic component prescribes an ARMA-like structure to model the conditional \(\rho \) th quantile by means of a link. Parameter estimation in the proposed model is performed using partial maximum likelihood, for which we provide closed formulas for the score vector and partial information matrix. We also discuss some inferential tools, such as the construction of confidence intervals, hypotheses testing, model selection, and forecasting. A Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed partial maximum likelihood approach. Finally, we examine the prediction power by contrasting our method with others in the literature using the Manufacturing Capacity Utilization from the US.
      PubDate: 2024-03-01
      DOI: 10.1007/s11749-023-00893-8
       
  • Statistical analysis of measures of non-convexity

    • Free pre-print version: Loading...

      Abstract: Abstract Several measures of non-convexity (departures from convexity) have been introduced in the literature, both for sets and functions. Some of them are of geometric nature, while others are more of topological nature. We address the statistical analysis of some of these measures of non-convexity of a set S, by dealing with their estimation based on a sample of points in S. We introduce also a new measure of non-convexity. We discuss briefly about these different notions of non-convexity, prove consistency and find the asymptotic distribution for the proposed estimators. We also consider the practical implementation of these estimators and illustrate their applicability to a real data example.
      PubDate: 2024-03-01
      DOI: 10.1007/s11749-023-00889-4
       
  • A statistical learning view of simple Kriging

    • Free pre-print version: Loading...

      Abstract: Abstract In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibiting a possibly complex spatial dependence structure are becoming increasingly available. In this context, the standard probabilistic theory of statistical learning does not apply directly and guarantees of the generalization capacity of predictive rules learned from such data are left to establish. We analyze here the simple Kriging task, the flagship problem in Geostatistics, from a statistical learning perspective, i.e., by carrying out a nonparametric finite-sample predictive analysis. Given \(d\ge 1\) values taken by a realization of a square integrable random field \(X=\{X_s\}_{s\in S}\) , \(S\subset {\mathbb {R}}^2\) , with unknown covariance structure, at sites \(s_1,\; \ldots ,\; s_d\) in S, the goal is to predict the unknown values it takes at any other location \(s\in S\) with minimum quadratic risk. The prediction rule being derived from a training spatial dataset: a single realization \(X'\) of X, is independent from those to be predicted, observed at \(n\ge 1\) locations \(\sigma _1,\; \ldots ,\; \sigma _n\) in S. Despite the connection of this minimization problem with kernel ridge regression, establishing the generalization capacity of empirical risk minimizers is far from straightforward, due to the non-independent and identically distributed nature of the training data \(X'_{\sigma _1},\; \ldots ,\; X'_{\sigma _n}\) involved in the learning procedure. In this article, non-asymptotic bounds of order \(O_{{\mathbb {P}}}(1/\sqrt{n})\) are proved for the excess risk of a plug-in predictive rule mimicking the true minimizer in the case of isotropic stationary Gaussian processes, observed at locations forming a regular grid in the learning stage. These theoretical results, as well as the role played by the technical conditions required to establish them, are illustrated by various numerical experiments, on simulated data and on real-world datasets, and hopefully pave the way for further developments in statistical learning based on spatial data.
      PubDate: 2024-03-01
      DOI: 10.1007/s11749-023-00891-w
       
  • Variable selection in function-on-scalar single-index model via the
           alternating direction method of multipliers

    • Free pre-print version: Loading...

      Abstract: Abstract We develop a new method for variable selection in a function-on-scalar single-index model. The proposed method goes beyond existing additive function-on-scalar regression framework and models dynamic effects of multiple scalar covariates via a varying coefficient single-index model. The unknown bivariate link function is modeled with splines. A computationally efficient alternating direction method of multipliers-based algorithm is used for simultaneous selection of the influential covariates and estimation of the single-index coefficients and the link function. The proposed method provides a flexible framework for variable selection in function-on-scalar regression, particularly in the presence of nonlinear and interaction effects. Numerical analysis using simulations illustrates satisfactory finite sample performance of the proposed method in terms of selection and estimation accuracy. An application is demonstrated on the CD4+ cell counts data. Software implementation of the proposed method is provided in R.
      PubDate: 2024-03-01
      DOI: 10.1007/s11749-023-00884-9
       
  • Correction to: LRD spectral analysis of multifractional functional time
           series on manifolds

    • Free pre-print version: Loading...

      PubDate: 2024-03-01
      DOI: 10.1007/s11749-024-00924-y
       
  • Comments on: Shape-based functional data analysis by Wu, Huang and
           Srivastava

    • Free pre-print version: Loading...

      PubDate: 2024-02-09
      DOI: 10.1007/s11749-023-00918-2
       
  • Comments on: Shape-based functional data analysis

    • Free pre-print version: Loading...

      Abstract: Abstract This discussion paper applauds the authors for their impactful contribution to functional data analysis (FDA). Their primary insight lies in a formal mathematical definition of the “shape” of a curve, which they connect to familiar intuitive notions through a number of examples. Notably, the paper highlights the pitfalls of less well-thought-out curve registration approaches. The authors’ application of COVID-19 data enriches the discussion, highlighting the work’s practical relevance. We discuss connections of this work with object-oriented data analysis and propose enhancements to the authors’ shape-based functional principal component analysis. Additionally, we illustrate the practical significance of adaptive alignment with an example from our own research.
      PubDate: 2024-01-18
      DOI: 10.1007/s11749-023-00914-6
       
  • Comments on: Shape-based functional data analysis

    • Free pre-print version: Loading...

      Abstract: Abstract The discussion focuses on the different choices that are made by the user in carrying out shape-based functional data analysis. First, there is the choice of an additional warping penalty that can be included in the procedure. An object-oriented data analysis approach can be useful for selecting such a warping penalty, and an example from monitoring peatland is given. Also, there is a choice to be made about whether the analysis is in a quotient manifold or an ambient space. There are advantages and disadvantages to either strategy, but in many examples, the results are similar due to a Laplace approximation. The final comment states that the authors provide plenty of convincing approaches with many useful insights. It is clear that the square root velocity function (SRVF) and transported SRVF methods will give solutions to many more problems in the future.
      PubDate: 2023-12-13
      DOI: 10.1007/s11749-023-00907-5
       
  • Comments on: Shape-based functional data analysis

    • Free pre-print version: Loading...

      PubDate: 2023-12-11
      DOI: 10.1007/s11749-023-00904-8
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.221.73.157
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-
JournalTOCs
 
 

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

              [Sort by number of followers]   [Restore default list]

  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 37)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Biometrics     Hybrid Journal   (Followers: 49)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Computational Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 38)
Current Research in Biostatistics     Open Access   (Followers: 9)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 9)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 8)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 19)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 11)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 85)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 7)
Journal of Probability and Statistics     Open Access   (Followers: 11)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 35)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 19, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 77, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 18)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 5)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 4)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 10)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Review of Economics and Statistics     Hybrid Journal   (Followers: 281)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 16)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Significance     Hybrid Journal   (Followers: 6)
Sociological Methods & Research     Hybrid Journal   (Followers: 49)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 144)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

              [Sort by number of followers]   [Restore default list]

Similar Journals
Similar Journals
HOME > Browse the 73 Subjects covered by JournalTOCs  
SubjectTotal Journals
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 44.221.73.157
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-