Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

PROBABILITIES AND MATH STATISTICS (113 journals)                     

Showing 1 - 85 of 85 Journals sorted alphabetically
Advances in Statistics     Open Access   (Followers: 10)
Afrika Statistika     Open Access   (Followers: 1)
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 13)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Data Science     Hybrid Journal   (Followers: 15)
Applied Medical Informatics     Open Access   (Followers: 12)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Asian Journal of Probability and Statistics     Open Access  
Austrian Journal of Statistics     Open Access   (Followers: 4)
Biostatistics & Epidemiology     Hybrid Journal   (Followers: 6)
Calcutta Statistical Association Bulletin     Hybrid Journal  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 3)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics: Case Studies, Data Analysis and Applications     Hybrid Journal  
Comunicaciones en Estadística     Open Access  
Econometrics and Statistics     Hybrid Journal   (Followers: 2)
Forecasting     Open Access   (Followers: 1)
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 2)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 10)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 14)
Indonesian Journal of Applied Statistics     Open Access  
International Game Theory Review     Hybrid Journal  
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
International Journal of Advanced Statistics and Probability     Open Access   (Followers: 7)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Ecological Economics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Game Theory     Hybrid Journal   (Followers: 3)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
International Journal of Multivariate Data Analysis     Hybrid Journal  
International Journal of Probability and Statistics     Open Access   (Followers: 3)
International Journal of Statistics & Economics     Full-text available via subscription   (Followers: 6)
International Journal of Statistics and Applications     Open Access   (Followers: 2)
International Journal of Statistics and Probability     Open Access   (Followers: 3)
International Journal of Statistics in Medical Research     Hybrid Journal   (Followers: 2)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Iraqi Journal of Statistical Sciences     Open Access  
Japanese Journal of Statistics and Data Science     Hybrid Journal  
Journal of Biometrics & Biostatistics     Open Access   (Followers: 4)
Journal of Cost Analysis and Parametrics     Hybrid Journal   (Followers: 5)
Journal of Environmental Statistics     Open Access   (Followers: 4)
Journal of Game Theory     Open Access   (Followers: 1)
Journal of Mathematical Economics and Finance     Full-text available via subscription  
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Modern Applied Statistical Methods     Open Access   (Followers: 1)
Journal of Official Statistics     Open Access   (Followers: 2)
Journal of Quantitative Economics     Hybrid Journal  
Journal of Social and Economic Statistics     Open Access   (Followers: 4)
Journal of Statistical Theory and Practice     Hybrid Journal   (Followers: 2)
Journal of Statistics and Data Science Education     Open Access   (Followers: 2)
Journal of Survey Statistics and Methodology     Hybrid Journal   (Followers: 6)
Journal of the Indian Society for Probability and Statistics     Full-text available via subscription  
Jurnal Biometrika dan Kependudukan     Open Access   (Followers: 1)
Lietuvos Statistikos Darbai     Open Access   (Followers: 1)
Mathematics and Statistics     Open Access   (Followers: 2)
Methods, Data, Analyses     Open Access   (Followers: 1)
METRON     Hybrid Journal   (Followers: 2)
Nepalese Journal of Statistics     Open Access   (Followers: 1)
North American Actuarial Journal     Hybrid Journal   (Followers: 2)
Open Journal of Statistics     Open Access   (Followers: 3)
Open Mathematics, Statistics and Probability Journal     Open Access  
Pakistan Journal of Statistics and Operation Research     Open Access   (Followers: 1)
Physica A: Statistical Mechanics and its Applications     Hybrid Journal   (Followers: 7)
Probability, Uncertainty and Quantitative Risk     Open Access   (Followers: 2)
Research & Reviews : Journal of Statistics     Open Access   (Followers: 4)
Revista Brasileira de Biometria     Open Access  
Revista Colombiana de Estadística     Open Access  
RMS : Research in Mathematics & Statistics     Open Access   (Followers: 1)
Sankhya B - Applied and Interdisciplinary Statistics     Hybrid Journal  
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 6)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 3)
Spatial Statistics     Hybrid Journal   (Followers: 2)
Stat     Hybrid Journal   (Followers: 1)
Stata Journal     Full-text available via subscription   (Followers: 10)
Statistica     Open Access   (Followers: 6)
Statistical Analysis and Data Mining     Hybrid Journal   (Followers: 23)
Statistical Theory and Related Fields     Hybrid Journal  
Statistics and Public Policy     Open Access   (Followers: 3)
Statistics in Transition New Series : An International Journal of the Polish Statistical Association     Open Access  
Statistics Research Letters     Open Access   (Followers: 1)
Statistics, Optimization & Information Computing     Open Access   (Followers: 5)
Stats     Open Access  
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Theory of Probability and Mathematical Statistics     Full-text available via subscription   (Followers: 2)
Turkish Journal of Forecasting     Open Access   (Followers: 1)
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal  

           

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Japanese Journal of Statistics and Data Science
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2520-8756 - ISSN (Online) 2520-8764
Published by Springer-Verlag Homepage  [2468 journals]
  • Bayesian and minimax estimators of loss

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      Abstract: Abstract We study the problem of loss estimation that involves for an observable \(X \sim f_{\theta }\) the choice of a first-stage estimator \(\hat{\gamma }\) of \(\gamma (\theta )\) , incurred loss \(L=L(\theta , \hat{\gamma })\) , and the choice of a second-stage estimator \(\hat{L}\) of L. We consider both: (i) a sequential version where the first-stage estimate and loss are fixed and optimization is performed at the second-stage level, and (ii) a simultaneous version with a Rukhin-type loss function designed for the evaluation of \((\hat{\gamma }, \hat{L})\) as an estimator of \((\gamma , L)\) . We explore various Bayesian solutions and provide minimax estimators for both situations (i) and (ii). The analysis is carried out for several probability models, including multivariate normal models \(N_d(\theta , \sigma ^2 I_d)\) with both known and unknown \(\sigma ^2\) , Gamma, univariate and multivariate Poisson, and negative binomial models, and relates to different choices of the first-stage and second-stage losses. The minimax findings are achieved by identifying a least favourable sequence of priors and depend critically on particular Bayesian solution properties, namely situations where the second-stage estimator \(\hat{L}(x)\) is constant as a function of x.
      PubDate: 2024-07-02
       
  • Dynamic factor models for claim reserving

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      Abstract: Abstract This study presents a new approach to claim reserving in the insurance industry using dynamic factor models (DFMs). Traditional methods often struggle to adapt to temporal variations in loss development, a gap that DFMs can effectively address. By employing DFMs on a multivariate time series of loss development factors (LDFs), we offer a more sensitive adaptation and understanding of loss development over time. Our methodology not only facilitates adjustment to trends in loss development but also provides clear explanations for the underlying reasons behind these trends. This aspect is crucial for actuaries, whose responsibilities include offering transparent and understandable reserve estimates. We apply the proposed DFMs to datasets from two different lines of business, demonstrating their ability to capture the temporal evolution of factors influencing loss development. The results indicate that our approach enhances fitting ability and provides deep insights into the dynamics of claim reserving. Furthermore, we assess the uncertainty in the ultimate loss amounts required for risk management to ensure financial stability and compliance with insurance regulatory requirements. This study contributes to the field of actuarial science by highlighting the potential of DFMs in enhancing the accuracy and reliability of claim reserving processes.
      PubDate: 2024-06-29
       
  • On the cost of risk misspecification in insurance pricing

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      Abstract: Abstract In the non-life insurance industry, pricing is often done relative to individual criteria of policyholders. Various classification algorithms are in use to categorize policyholders into risk classes defined by the insurer, but classification errors may result from this process. In the light of recent automatic classification practices, it becomes important to assess the risks caused by such errors. In this paper we examine the impact of risk class misspecifications for a simple situation with two risk types. We provide a mean-variance framework for quantitatively studying the insurer’s optimization problem of specifying premiums and we analyze the tradeoff of costs and benefits when classification error probabilities are known.
      PubDate: 2024-06-25
       
  • Bayesian projection of cohort healthy life expectancies with long-term
           care insurance data

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      Abstract: Abstract Over the past few decades, life expectancy has increased across the world. However, such a rise in longevity appears to be accompanied by a decline in health at advanced ages. This paper investigates future changes in longevity and health status at advanced ages and their interdependence by constructing a multinomial logit model using Japan’s public long-term care insurance data. Given that the decline in mortality at older ages continues, healthy life expectancy has attracted attention as an indicator of good health and well-being instead of life expectancy. We project cohort healthy life expectancies at advanced ages using the smultinomial logit model. We adopt a Bayesian approach to take prediction error into account.
      PubDate: 2024-06-11
       
  • Likelihood-based instrumental variable methods for Cox proportional
           hazards model

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      Abstract: Abstract The Cox proportional hazards model is widely used for analyzing time-to-event outcomes while adjusting for covariates. However, obtaining a consistent estimator can be challenging if certain covariates remain unobserved. Instrumental variable (IV) methods are frequently employed to address unmeasured covariates; however, standard IV methods cannot be applied to time-to-event outcomes without modification. In this study, we propose extending IV methods capable of handling binary, multivalue, or continuous treatments, as well as non-binary and multiple IVs, which has not been achieved by previous methods. Our approach leverages the frailty model to directly capture the variability of unmeasured covariates and allows for flexible treatment models. The proposed method outperforms previous approaches, as demonstrated by theoretical considerations and numerical experiments. Furthermore, we performed a real data analysis using health insurance claims data from Hiroshima Prefecture, Japan. The proposed method is expected to address the issue of unmeasured covariates in time-to-event outcomes across a wider range of scenarios. Additionally, it can serve as a type of sensitivity analysis to assess the influence of unmeasured covariates.
      PubDate: 2024-06-06
       
  • Test for high-dimensional outliers with principal component analysis

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      Abstract: Abstract We herein consider a test of outlier detection for high-dimensional, low-sample-size (HDLSS) data. Although outlier detection is a fundamental problem, it has not been extensively studied in the HDLSS setting. We derive asymptotic properties of the first principal component scores with outliers. We consider high-dimensional outlier detection by applying the asymptotic properties to the Grubbs test, a well-known method for testing outliers. Our results indicate that the test statistic provides preferable performance for both the size and power. Using this test procedure, we propose an algorithm to identify multiple outliers. We present an investigation of the theoretical properties of a sure independent screening and it can achieve complete identification of the outliers with high accuracy. Finally, we investigate the performance for both numerical studies and real data analyses as compared to available outlier detection methods in HDLSS settings. The proposed method exhibits superiority in terms of not only correctly detecting outliers, but also identifying a number of false identifications.
      PubDate: 2024-06-04
       
  • Semiparametric regression analysis of doubly censored recurrent event data

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      Abstract: Abstract Recurrent event data are common in survival and reliability studies, where a subject experiences the same type of event repeatedly. There are situations, in which the event of interest can be observed only if they belong to a window of observational range, leading to double censoring of recurrent event times. In this paper, we study recurrent event data subject to double censoring. We propose a proportional mean model for the analysis of doubly censored recurrent event data based on the mean function of the underlying recurrent event process. The estimators of the regression parameters and the baseline mean function are derived and their asymptotic properties are studied. A Monte Carlo simulation study is conducted to assess the finite sample behavior of the proposed estimators. Finally, the procedures are illustrated using two real-life data sets, one from a bladder cancer study and the other from a study on chronic granulomatous disease.
      PubDate: 2024-06-01
       
  • Non-minimaxity of debiased shrinkage estimators

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      Abstract: Abstract We consider the estimation of the p-variate normal mean of \(X\sim \mathcal {N}_p(\theta ,I)\) under the quadratic loss function. We investigate the decision theoretic properties of debiased shrinkage estimator, the estimator which shrinks towards the origin for smaller \(\Vert x\Vert ^2\) and which is exactly equal to the unbiased estimator X for larger \(\Vert x\Vert ^2\) . Such debiased shrinkage estimator seems superior to the unbiased estimator X, which implies minimaxity. However, we show that it is not minimax under mild conditions.
      PubDate: 2024-06-01
       
  • Shrinkage estimation with logarithmic penalties

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      Abstract: Abstract In this paper, we have developed a novel approach for deriving shrinkage estimators of means without assuming normality. Our method is based on the equation of the first-order condition with a logarithmic penalty, and it introduces both one-step and two-step shrinkage estimators. The one-step estimator closely resembles the James–Stein estimator, while the differentiable two-step estimator exhibits similar performance to the positive-part Stein estimator. Although the latter does not satisfy Baranchik’s conditions, both estimators can be demonstrated to be minimax under normality assumptions. Furthermore, we have extended this method to handle cases involving an unknown scale. We have successfully applied this approach to the simultaneous estimation of Poisson means.
      PubDate: 2024-06-01
       
  • Bivariate dynamic weighted cumulative residual entropy

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      Abstract: Abstract As a way to measure uncertainty, the cumulative residual entropy (CRE) was initially developed. Later came the dynamic version of CRE, called dynamic cumulative residual entropy (DCRE). The bivariate dynamic cumulative residual entropy (BDCRE), a more sophisticated version of this measure, was also presented. In this study, we introduce and investigate the features of a weighted version of BDCRE.
      PubDate: 2024-06-01
       
  • An adaptive singular value shrinkage for estimation problem of low-rank
           matrix mean with unknown covariance matrix

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      Abstract: Abstract Matrix models which are constructed from a deterministic signal plus noise are used in variety of fields. In particular, it commonly happens that a signal matrix is of low-rank, however, the rank itself is unknown. In this paper, it is assumed that the noises are distributed as real normal or complex normal distributions, across columns are independently and identically distributed, and across rows of noise matrix are correlated. But the covariance matrix is unknown. For the problem of estimating a low-rank matrix, estimators with soft-thresholding singular values are considered and a formula to choose a threshold is proposed based on the celebrated SURE method and eigenvalue-calculus due to Stein (Proceedings of the Prague symposium on asymptotic statistics, vol II. Charles University, Prague, pp 345–381, 1973) who employed these methods for matrix estimation problem. Very few results are obtained here. However, the procedures proposed are expected to be useful for situations to occur in practice.
      PubDate: 2024-06-01
       
  • Machine learning and the James–Stein estimator

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      Abstract: Abstract It is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made a spectacular first impression on the statistical community through its demonstration of inadmissability of the maximum likelihood estimator. It continues to be influential, but not for the initial reasons. Empirical Bayes shrinkage estimation, now a major topic, found its early justification in the James–Stein formula. Less obvious downstream topics include Tweedie’s formula and Benjamini and Hochberg’s false discovery rate algorithm. This is a short and mainly non-technical review of the James–Stein rule and its effects on the machine learning era of statistical innovation.
      PubDate: 2024-06-01
       
  • Sparse inference of structural equation modeling with latent variables for
           diffusion processes

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      Abstract: Abstract We consider structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. The quasi-likelihood estimators for parameters in the SEM are proposed. The goodness-of-fit test is derived from the quasi-likelihood ratio. We also treat sparse inference in the SEM. The goodness-of-fit test for the sparse inference in the SEM is developed. Furthermore, the asymptotic properties of our proposed estimators and test statistics are examined.
      PubDate: 2024-06-01
       
  • Matrix quadratic risk of orthogonally invariant estimators for a normal
           mean matrix

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      Abstract: Abstract In estimation of a normal mean matrix under the matrix quadratic loss, we develop a general formula for the matrix quadratic risk of orthogonally invariant estimators. The derivation is based on several formulas for matrix derivatives of orthogonally invariant functions of matrices. As an application, we calculate the matrix quadratic risk of a singular value shrinkage estimator motivated by Stein’s proposal for improving on the Efron–Morris estimator 50 years ago.
      PubDate: 2024-06-01
       
  • Variable selection through adaptive elastic net for proportional odds
           model

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      Abstract: Abstract In this paper, we propose a method for fitting the proportional odds model by maximizing the marginal likelihood while incorporating an elastic net penalty. We assign adaptive weights to different coefficients, allowing important variables to receive smaller penalties and be more protectively retained in the final model, while unimportant variables receive larger penalties and are more likely to be eliminated. This approach combines the strengths of adaptively weighted LASSO shrinkage and quadratic regularization, resulting in optimal large sample performance and the ability to effectively handle collinearity. We also present a computational algorithm for the proposed method and compare its performance to that of LASSO, elastic net, and adaptive LASSO through simulation studies and applications to real datasets. The results demonstrate that the proposed method tends to perform better than existing approaches.
      PubDate: 2024-06-01
       
  • Estimating the effective reproduction number of COVID-19 via the chain
           ladder method

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      Abstract: Abstract This paper addressed a critical issue of reporting delays in estimating the effective reproduction number, focusing on the context of the COVID-19 pandemic. The reporting delay problem is a pervasive challenge, impacting the accuracy of the estimation and consequently influencing public health decision-making. Through the exploration of the application of the Chain Ladder method, a well-established technique from actuarial science, a novel approach to mitigate the effects of reporting delays in infectious disease epidemiology was proposed. By applying the Chain Ladder method to infectious disease data, we illustrated its potential to provide more accurate and timely estimation, accounting for reporting delays inherent in epidemiological surveillance systems.
      PubDate: 2024-06-01
       
  • Special feature: Stein estimation and statistical shrinkage methods

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      PubDate: 2024-05-11
       
  • Making statistical inferences about linkage errors

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      Abstract: Abstract Record linkage aims to identify records that are from the same unit, in one or many sources. Sometimes, it is imperfect because the available identifying information is limited and erroneous. In such cases, it is important to report the linkage accuracy, which may be measured according to one of many proposed statistical models. These models offer clear advantages over clerical reviews, in terms of costs and timeliness. They also apply where clerical reviews are impossible, e.g., when two parties need to link their respective data sets, such that neither party can see the record pairs in the clear. For obvious reasons, these models must be validated before they are used, by performing goodness-to-fit tests. Unfortunately, this is currently difficult because all existing models rely on observations that are correlated. Thus, the Chi-squared and likelihood ratio tests are biased. In fact, it is challenging to perform any kind of statistical inference about these models or their parameters. In this work, this long-standing problem is addressed when modeling the linkage errors through the number of links of a record. The proposed solution bases the inferences on a subset of observations that are approximately independent.
      PubDate: 2024-02-25
      DOI: 10.1007/s42081-023-00228-9
       
  • Outlier-robust parameter estimation for unnormalized statistical models

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      Abstract: Abstract Unnormalized statistical models are ubiquitous in modern statistical data analysis. Recent methods take a classification approach to estimate unnormalized models. However, the classification problem is often solved based on the maximum-likelihood estimation, which can be seriously hampered by the contamination of outliers. In this paper, we propose two outlier-robust methods for estimation of unnormalized statistical models. The proposed methods are developed by combining robust divergences with the classification approach, and their robustness is theoretically investigated based on influence function. Interestingly, our theoretical analysis reveals a counter-intuitive robustness of the proposed methods, and shows the importance of not only employing robust divergences but also taking the classification approach for outlier-robust estimation. Finally, we experimentally demonstrate that the proposed methods are robust against outliers.
      PubDate: 2024-02-06
      DOI: 10.1007/s42081-023-00237-8
       
  • Stein’s identities and the related topics: an instructive explanation on
           shrinkage, characterization, normal approximation and goodness-of-fit

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      Abstract: Abstract The Stein-type identities are widely recognized for their substantial utility and potency in deriving shrinkage estimators improving on crude estimators in normal, gamma, Poisson, and negative binomial distributions. Additionally, these identities serve to characterize these distributions themselves. The Stein identities are also used to demonstrate normal approximation. Moreover, they are instrumental in constructing statistical tests to assess the goodness-of-fit for normality, exponentiality, and Poissonity of distributions. This article offers an instructive and comprehensive explanation of the applications of Stein-type identities in the aforementioned contexts.
      PubDate: 2024-01-31
      DOI: 10.1007/s42081-023-00239-6
       
 
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  Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

PROBABILITIES AND MATH STATISTICS (113 journals)                     

Showing 1 - 85 of 85 Journals sorted alphabetically
Advances in Statistics     Open Access   (Followers: 10)
Afrika Statistika     Open Access   (Followers: 1)
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 13)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Data Science     Hybrid Journal   (Followers: 15)
Applied Medical Informatics     Open Access   (Followers: 12)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Asian Journal of Probability and Statistics     Open Access  
Austrian Journal of Statistics     Open Access   (Followers: 4)
Biostatistics & Epidemiology     Hybrid Journal   (Followers: 6)
Calcutta Statistical Association Bulletin     Hybrid Journal  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 3)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics: Case Studies, Data Analysis and Applications     Hybrid Journal  
Comunicaciones en Estadística     Open Access  
Econometrics and Statistics     Hybrid Journal   (Followers: 2)
Forecasting     Open Access   (Followers: 1)
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 2)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 10)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 14)
Indonesian Journal of Applied Statistics     Open Access  
International Game Theory Review     Hybrid Journal  
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
International Journal of Advanced Statistics and Probability     Open Access   (Followers: 7)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Ecological Economics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Game Theory     Hybrid Journal   (Followers: 3)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
International Journal of Multivariate Data Analysis     Hybrid Journal  
International Journal of Probability and Statistics     Open Access   (Followers: 3)
International Journal of Statistics & Economics     Full-text available via subscription   (Followers: 6)
International Journal of Statistics and Applications     Open Access   (Followers: 2)
International Journal of Statistics and Probability     Open Access   (Followers: 3)
International Journal of Statistics in Medical Research     Hybrid Journal   (Followers: 2)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Iraqi Journal of Statistical Sciences     Open Access  
Japanese Journal of Statistics and Data Science     Hybrid Journal  
Journal of Biometrics & Biostatistics     Open Access   (Followers: 4)
Journal of Cost Analysis and Parametrics     Hybrid Journal   (Followers: 5)
Journal of Environmental Statistics     Open Access   (Followers: 4)
Journal of Game Theory     Open Access   (Followers: 1)
Journal of Mathematical Economics and Finance     Full-text available via subscription  
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Modern Applied Statistical Methods     Open Access   (Followers: 1)
Journal of Official Statistics     Open Access   (Followers: 2)
Journal of Quantitative Economics     Hybrid Journal  
Journal of Social and Economic Statistics     Open Access   (Followers: 4)
Journal of Statistical Theory and Practice     Hybrid Journal   (Followers: 2)
Journal of Statistics and Data Science Education     Open Access   (Followers: 2)
Journal of Survey Statistics and Methodology     Hybrid Journal   (Followers: 6)
Journal of the Indian Society for Probability and Statistics     Full-text available via subscription  
Jurnal Biometrika dan Kependudukan     Open Access   (Followers: 1)
Lietuvos Statistikos Darbai     Open Access   (Followers: 1)
Mathematics and Statistics     Open Access   (Followers: 2)
Methods, Data, Analyses     Open Access   (Followers: 1)
METRON     Hybrid Journal   (Followers: 2)
Nepalese Journal of Statistics     Open Access   (Followers: 1)
North American Actuarial Journal     Hybrid Journal   (Followers: 2)
Open Journal of Statistics     Open Access   (Followers: 3)
Open Mathematics, Statistics and Probability Journal     Open Access  
Pakistan Journal of Statistics and Operation Research     Open Access   (Followers: 1)
Physica A: Statistical Mechanics and its Applications     Hybrid Journal   (Followers: 7)
Probability, Uncertainty and Quantitative Risk     Open Access   (Followers: 2)
Research & Reviews : Journal of Statistics     Open Access   (Followers: 4)
Revista Brasileira de Biometria     Open Access  
Revista Colombiana de Estadística     Open Access  
RMS : Research in Mathematics & Statistics     Open Access   (Followers: 1)
Sankhya B - Applied and Interdisciplinary Statistics     Hybrid Journal  
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 6)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 3)
Spatial Statistics     Hybrid Journal   (Followers: 2)
Stat     Hybrid Journal   (Followers: 1)
Stata Journal     Full-text available via subscription   (Followers: 10)
Statistica     Open Access   (Followers: 6)
Statistical Analysis and Data Mining     Hybrid Journal   (Followers: 23)
Statistical Theory and Related Fields     Hybrid Journal  
Statistics and Public Policy     Open Access   (Followers: 3)
Statistics in Transition New Series : An International Journal of the Polish Statistical Association     Open Access  
Statistics Research Letters     Open Access   (Followers: 1)
Statistics, Optimization & Information Computing     Open Access   (Followers: 5)
Stats     Open Access  
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Theory of Probability and Mathematical Statistics     Full-text available via subscription   (Followers: 2)
Turkish Journal of Forecasting     Open Access   (Followers: 1)
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal  

           

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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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