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 - 98 of 98 Journals sorted alphabetically
Advances in Statistics     Open Access   (Followers: 9)
Afrika Statistika     Open Access   (Followers: 1)
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 11)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Data Science     Hybrid Journal   (Followers: 14)
Annual Review of Statistics and Its Application     Full-text available via subscription   (Followers: 7)
Applied Medical Informatics     Open Access   (Followers: 11)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Asian Journal of Probability and Statistics     Open Access  
Austrian Journal of Statistics     Open Access   (Followers: 4)
Biostatistics & Epidemiology     Hybrid Journal   (Followers: 4)
Cadernos do IME : Série Estatística     Open Access  
Calcutta Statistical Association Bulletin     Hybrid Journal  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 4)
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: 1)
Forecasting     Open Access   (Followers: 1)
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 3)
Frontiers in Applied Mathematics and Statistics     Open Access   (Followers: 1)
Game Theory     Open Access   (Followers: 2)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 14)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 13)
Indonesian Journal of Applied Statistics     Open Access  
International Game Theory Review     Hybrid Journal   (Followers: 1)
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 Algebra and Statistics     Open Access   (Followers: 3)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 3)
International Journal of Ecological Economics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Energy and Statistics     Hybrid Journal   (Followers: 3)
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: 4)
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: 5)
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  
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: 4)
Journal of the Indian Society for Probability and Statistics     Full-text available via subscription  
Jurnal Biometrika dan Kependudukan     Open Access  
Jurnal Ekonomi Kuantitatif Terapan     Open Access  
Jurnal Sains Matematika dan Statistika     Open Access  
Lietuvos Statistikos Darbai     Open Access  
Mathematics and Statistics     Open Access   (Followers: 2)
Methods, Data, Analyses     Open Access   (Followers: 1)
METRON     Hybrid Journal   (Followers: 1)
Nepalese Journal of Statistics     Open Access  
North American Actuarial Journal     Hybrid Journal   (Followers: 1)
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: 6)
Probability, Uncertainty and Quantitative Risk     Open Access   (Followers: 2)
Ratio Mathematica     Open Access  
Research & Reviews : Journal of Statistics     Open Access   (Followers: 3)
Revista Brasileira de Biometria     Open Access  
Revista Colombiana de Estadística     Open Access  
RMS : Research in Mathematics & Statistics     Open Access  
Romanian Statistical Review     Open Access  
Sankhya B - Applied and Interdisciplinary Statistics     Hybrid Journal  
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 1)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 2)
Spatial Statistics     Hybrid Journal   (Followers: 2)
Sri Lankan Journal of Applied Statistics     Open Access  
Stat     Hybrid Journal   (Followers: 1)
Stata Journal     Full-text available via subscription   (Followers: 8)
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: 4)
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: 3)
Stats     Open Access  
Synthesis Lectures on Mathematics and Statistics     Full-text available via subscription   (Followers: 1)
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)
VARIANSI : Journal of Statistics and Its application on Teaching and Research     Open Access  
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  [2469 journals]
  • Correction to: An integrated framework for visualizing and forecasting
           realized covariance matrices

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      PubDate: 2022-04-25
       
  • On the consistent estimation of linkage errors without training data

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      Abstract: Abstract In official statistics, record linkage is used to find records from the same entity in many data sources, often without a unique identifier. Consequently, linkage errors arise that are commonly measured by the recall and the precision; two finite population parameters that require the identification of all the record pairs, where the records are from the same entity. Given the scarcity and prohibitive costs of training data, an important practical question is whether these accuracy measures may be estimated with a model instead and if so which one to use. In a large part, the answer depends on whether the two accuracy measures satisfy a law of large numbers, in which case one may choose a consistent model-based estimator of the corresponding limits. Yet establishing such a law of large numbers is challenging because the underlying observations have a complicated dependence structure. This paper settles the question by actually showing the convergence of the two accuracy measures and the consistency of a few estimators that model the number of links adjacent to a given record, under general conditions. For practical applications, it also evaluates the finite sample performance of these estimators through simulations and an experiment with public census data.
      PubDate: 2022-04-23
       
  • Bayesian nonparametric quantile mixed-effects models via regularization
           using Gaussian process priors

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      Abstract: Abstract In this study, we proposed using Bayesian nonparametric quantile mixed-effects models (BNQMs) to estimate the nonlinear structure of quantiles in hierarchical data. Assuming that a nonlinear function representing a phenomenon of interest cannot be specified in advance, a BNQM can estimate the nonlinear function of quantile features using the basis expansion method. Furthermore, BNQMs adjust the smoothness to prevent overfitting by regularization. We also proposed a Bayesian regularization method using Gaussian process priors for the coefficient parameters of the basis functions, and showed that the problem of overfitting can be reduced when the number of basis functions is excessive for the complexity of the nonlinear structure. Although computational cost is often a problem in quantile regression modeling, BNQMs ensure the computational cost is not too high using a fully Bayesian method. Using numerical experiments, we showed that the proposed model can estimate nonlinear structures of quantiles from hierarchical data more accurately than the comparison models in terms of mean squared error. Finally, to determine the cortisol circadian rhythm in infants, we applied a BNQM to longitudinal data of urinary cortisol concentration collected at Kurume University. The result suggested that infants have a bimodal cortisol circadian rhythm before their biological rhythms are established.
      PubDate: 2022-04-19
       
  • Real world data and data science in medical research: present and future

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      Abstract: Abstract Real world data (RWD) are generating greater interest in recent times despite being not new. There are various purposes of the RWD analytics in medical research as follows: effectiveness and safety of medical treatment, epidemiology such as incidence and prevalence of disease, burden of disease, quality of life and activity of daily living, medical costs, etc. The RWD research in medicine is a mixture of digital transformation, statistics or data science, public health, and regulatory science. Most of the articles describing the RWD or real-world evidence (RWE) in medical research cover only a portion of these specializations, which might lead to an incomplete understanding of the RWD. This article summarizes the overview and challenges of the RWD analysis in medical fields from methodological perspectives. As the first step for the RWD analysis, data source of the RWD should be comprehended. The progress of the RWD is closely related to the digitization, especially of medical administrative data and medical records. Second, the selection of appropriate statistical and epidemiological methods is highly critical for an RWD analysis than those for randomized clinical trials. This is because it contains greater varieties of bias, which should be controlled by balancing the underlying risk between treatment groups. Last, the future of the RWD is discussed in terms of overcoming limited data by proxy confounders, using unstructured text data, linking of multiple databases, using the RWD or RWE for a regulatory purpose, and evaluating values and new aspects in medical research brought by the RWD.
      PubDate: 2022-04-13
       
  • A positive-definiteness-assured block Gibbs sampler for Bayesian graphical
           models with shrinkage priors

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      Abstract: Abstract Although the block Gibbs sampler for the Bayesian graphical LASSO proposed by Wang (2012) has been widely applied and extended to various shrinkage priors in recent years, it has a less noticeable but possibly severe disadvantage that the positive definiteness of a precision matrix in the Gaussian graphical model is not guaranteed in each cycle of the Gibbs sampler. Specifically, if the dimension of the precision matrix exceeds the sample size, the positive definiteness of the precision matrix will be barely satisfied and the Gibbs sampler will almost surely fail. In this paper, we propose modifying the original block Gibbs sampler so that the precision matrix never fails to be positive definite by sampling it exactly from the domain of the positive definiteness. As we have shown in the Monte Carlo experiments, this modification not only stabilizes the sampling procedure but also significantly improves the performance of the parameter estimation and graphical structure learning. We also apply our proposed algorithm to a graphical model of the monthly return data in which the number of stocks exceeds the sample period, demonstrating its stability and scalability.
      PubDate: 2022-04-13
       
  • Correction to: Spatial analysis and visualization of global data on
           multi-resolution hexagonal grids

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      PubDate: 2022-04-09
       
  • Experience of distance education for project-based learning in data
           science

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      Abstract: Abstract Data science plays an important role in many fields. Project-based learning is an effective teaching approach because students can learn data science practices based on real-world problems and real-world data. Because of a pandemic of COVID-19, we provided project-based learning as distance education (synchronic distance education). In this study, we explain how we developed and conducted it and provide survey results from students. The survey showed about 30% of the students found it difficult to communicate with each other and with teachers. However, it suggested that they could communicate to some extent even by remote group work. We found that, in remote communication, it is important to see the faces of all the students (and teachers) on the Zoom screen when they discuss using screen sharing. There remain some challenges such as timing to start talking and casual questions to teachers. Although some issues should be improved, distance education for project-based learning in data science can be implemented effectively.
      PubDate: 2022-04-09
       
  • Converting ADMM to a proximal gradient for efficient sparse estimation

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      Abstract: Abstract In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the problem. It takes time to include matrix division in the former case, while an efficient method such as FISTA (fast iterative shrinkage-thresholding algorithm) has been developed in the latter case. This paper proposes a general method for converting the ADMM solution to the proximal gradient method, assuming that assumption that the derivative of the objective function is Lipschitz continuous. Then, we apply it to sparse estimation problems, such as sparse convex clustering and trend filtering, and we show by numerical experiments that we can obtain a significant improvement in terms of efficiency.
      PubDate: 2022-04-01
       
  • Correction to: Statistical data integration in survey sampling: a review

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      PubDate: 2022-03-28
       
  • Shiga University’s endeavor to promote human resources development
           for data science in Japan

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      Abstract: Abstract In 2017, Shiga University established the Faculty of Data Science, which was the first faculty in Japan specializing in data science and statistics. This paper reports the Faculty’s historical context, curricula, and collaboration with industry and other universities. The career paths of the graduates and the massive open online courses and textbooks provided by the Faculty of Data Science are also summarized.
      PubDate: 2022-03-27
       
  • Use of primary decomposition of polynomial ideals arising from indicator
           functions to enumerate orthogonal fractions

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      Abstract: Abstract A polynomial indicator function of designs is first introduced by Fontana et al. (J Stat Plan Inference 87:149–172, 2000) for two-level cases. They give the structure of the indicator functions, especially the relation to the orthogonality of designs. These results are generalized by Aoki (J Stat Plan Inference 203:91–105, 2019) for general multi-level cases. As an application of these results, we can enumerate all orthogonal fractional factorial designs with given size and orthogonality using computational algebraic software. For example, Aoki (2019) gives classifications of orthogonal fractions of \(2^4\times 3\) designs with strength 3, which is derived by simple eliminations of variables. However, the computational feasibility of this naive approach depends on the size of the problems. In fact, it is reported that the computation of orthogonal fractions of \(2^4\times 3\) designs with strength 2 fails to carry out in Aoki (2019). In this paper, using the theory of primary decomposition, we enumerate and classify orthogonal fractions of \(2^4\times 3\) designs with strength 2. We show there are 35,200 orthogonal half fractions of \(2^4\times 3\) designs with strength 2, classified into 63 equivalent classes.
      PubDate: 2022-03-03
      DOI: 10.1007/s42081-022-00149-z
       
  • A k-means method for trends of time series

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      Abstract: Abstract A k-means method style clustering algorithm is proposed for trends of multivariate time series. The usual k-means method is based on distances or dissimilarity measures among multivariate data and centroids of clusters. Some similarity or dissimilarity measures are also available for multivariate time series. However, suitability of dissimilarity measures depends on the properties of time series. Moreover, it is not easy to define the centroid for time series. The k-medoid clustering method can be applied to time series using one of dissimilarity measures without using centroids. However, the k-medoid method becomes restrictive if appropriate medoids do not exist. In this paper, the centroid is defined as a common trend and a dissimilarity measure is also introduced for trends. Based on these centroids and dissimilarity measures, a k-means method style algorithm is proposed for a multivariate trend. The proposed method is applied to the time series of COVID-19 cases in each prefecture of Japan.
      PubDate: 2022-03-03
      DOI: 10.1007/s42081-022-00148-0
       
  • A weighted score confidence interval for a binomial proportion

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      Abstract: Abstract An alternative method of constructing a confidence interval for a binomial proportion is proposed. The proposed method, known as the weighted score interval, is obtained by applying a weight to the score interval leading to shortening or widening of the score interval depending on the choice of the weight. The weighted score interval is a general form of the score interval and is equivalent to the score interval when the weight is taken to be one. When an appropriate weight is chosen, simulation results indicate that the proposed interval performs better than the standard, Agresti-Coull and score intervals in terms of mean coverage probability and mean absolute error.
      PubDate: 2022-02-16
      DOI: 10.1007/s42081-022-00146-2
       
  • Improving kernel-based nonparametric regression for circular–linear
           data

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      Abstract: Abstract We discuss kernel-based nonparametric regression where a predictor has support on a circle and a responder has support on a real line. Nonparametric regression is used in analyzing circular–linear data because of its flexibility. However, nonparametric regression is generally less accurate than an appropriate parametric regression for a population model. Considering that statisticians need more accurate nonparametric regression models, we investigate the performance of sine series local polynomial regression while selecting the most suitable kernel class. The asymptotic result shows that higher-order estimators reduce conditional bias; however, they do not improve conditional variance. We show that higher-order estimators improve the convergence rate of the weighted conditional mean integrated square error. We also prove the asymptotic normality of the estimator. We conduct a numerical experiment to examine a small sample of characteristics of the estimator in scenarios wherein the error term is homoscedastic or heterogeneous. The result shows that choosing a higher degree improves performance under the finite sample in homoscedastic or heterogeneous scenarios. In particular, in some scenarios where the regression function is wiggly, higher-order estimators perform significantly better than local constant and linear estimators.
      PubDate: 2022-01-31
      DOI: 10.1007/s42081-022-00145-3
       
  • Analysis of COVID-19 evolution based on testing closeness of sequential
           data

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      Abstract: Abstract A practical algorithm has been developed for closeness analysis of sequential data that combines closeness testing with algorithms based on the Markov chain tester. It was applied to reported sequential data for COVID-19 to analyze the evolution of COVID-19 during a certain time period (week, month, etc.).
      PubDate: 2022-01-29
      DOI: 10.1007/s42081-021-00144-w
       
  • Inferences on cumulative incidence function for middle censored survival
           data with Weibull regression

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      Abstract: Abstract This article considers the problem of competing risks analysis in the presence of middle censoring scheme. In this censoring, the exact lifetime of the subject under study becomes unobservable when it falls in a random censoring interval and the associated competing causes of failure can be identified in later inspection. We consider the cumulative incidence function for evaluation of competing risks by assuming a Weibull cause specific hazard function with the common shape parameter for all competing causes. Maximum likelihood and midpoint approximation methods are employed for point estimation of unknown parameters and cumulative incidence functions. Asymptotic confidence intervals of maximum likelihood estimators are computed. In addition, the Bayes estimators and the associated credible intervals are also considered. The Monte Carlo simulation and an illustrative example on bone marrow transplant data are given for comprehensive comparison of proposed methods.
      PubDate: 2022-01-14
      DOI: 10.1007/s42081-021-00142-y
       
  • Spatial analysis of subjective well-being in Japan

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      Abstract: Abstract This study investigates subjective well-being in Japan using a survey of 22,539 respondents in 46 prefectures in December 2019. We applied a Bayesian hierarchical model to the self-reported well-being respondents, supposing that well-being is decomposed into regional and individual factors. As a result, regional heteroscedasticity and individual factors are identified jointly, which clarifies the interesting features of Japanese subjective well-being. From the identified regional factors in prefectural levels, we find that Social Welfare Expenditure (SWE) per capita and Ratio of Forest Area (RFA) are positively related to subjective well-being. Some prefectures in Capital Region, which are at the bottom of happiness ranking, are correlated with lower SWE and FRA. In addition, coastal areas in Tohoku region damaged by the 2011 tsunami and nuclear plant accidents also have relatively lower subjective well-being. This finding suggests that residents in the regions have not recovered and require additional mental and physical public support.
      PubDate: 2022-01-06
      DOI: 10.1007/s42081-021-00143-x
       
  • Marked point processes and intensity ratios for limit order book modeling

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      Abstract: Abstract This paper extends the analysis of Muni Toke and Yoshida (2020) to the case of marked point processes. We consider multiple marked point processes with intensities defined by three multiplicative components, namely a common baseline intensity, a state-dependent component specific to each process, and a state-dependent component specific to each mark within each process. We show that for specific mark distributions, this model is a combination of the ratio models defined in Muni Toke and Yoshida (2020). We prove convergence results for the quasi-maximum and quasi-Bayesian likelihood estimators of this model and provide numerical illustrations of the asymptotic variances. We use these ratio processes to model transactions occurring in a limit order book. Model flexibility allows us to investigate both state-dependency (emphasizing the role of imbalance and spread as significant signals) and clustering. Calibration, model selection and prediction results are reported for high-frequency trading data on multiple stocks traded on Euronext Paris. We show that the marked ratio model outperforms other intensity-based methods (such as “pure” Hawkes-based methods) in predicting the sign and aggressiveness of market orders on financial markets.
      PubDate: 2022-01-01
      DOI: 10.1007/s42081-021-00137-9
       
  • Special feature: Recent statistical methods for survival analysis

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      PubDate: 2021-12-01
      DOI: 10.1007/s42081-021-00140-0
       
  • Correction to: Inference on high-dimensional mean vectors under the
           strongly spiked eigenvalue model

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      Abstract: The article Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model
      PubDate: 2021-07-07
      DOI: 10.1007/s42081-021-00130-2
       
 
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