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  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)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Biometrics     Hybrid Journal   (Followers: 51)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 17)
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: 11)
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
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: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
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: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 38, 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: 82)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 23)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
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: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 16)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 138)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 17)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 40)
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: 8)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
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: 13)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 122)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
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: 10)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
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)

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Similar Journals
Journal Cover
Journal of Statistical Software
Journal Prestige (SJR): 13.802
Citation Impact (citeScore): 16
Number of Followers: 16  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1548-7660 - ISSN (Online) 1548-7660
Published by American Statistical Association Homepage  [5 journals]
  • More on Multidimensional Scaling and Unfolding in R: smacof Version 2

    • Authors: Patrick Mair; Patrick J. F. Groenen, Jan de Leeuw
      Abstract: The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications.
      PubDate: Fri, 13 May 2022 00:00:00 +000
       
  • covsim: An R Package for Simulating Non-Normal Data for Structural
           Equation Models Using Copulas

    • Authors: Steffen Grønneberg; Njål Foldnes, Katerina M. Marcoulides
      Abstract: In factor analysis and structural equation modeling non-normal data simulation is traditionally performed by specifying univariate skewness and kurtosis together with the target covariance matrix. However, this leaves little control over the univariate distributions and the multivariate copula of the simulated vector. In this paper we explain how a more flexible simulation method called vine-to-anything (VITA) may be obtained from copula-based techniques, as implemented in a new R package, covsim. VITA is based on the concept of a regular vine, where bivariate copulas are coupled together into a full multivariate copula. We illustrate how to simulate continuous and ordinal data for covariance modeling, and how to use the new package discnorm to test for underlying normality in ordinal data. An introduction to copula and vine simulation is provided in the appendix.
      PubDate: Mon, 02 May 2022 00:00:00 +000
       
  • rags2ridges: A One-Stop-ℓ2-Shop for Graphical Modeling of
           High-Dimensional Precision Matrices

    • Authors: Carel F. W. Peeters; Anders Ellern Bilgrau, Wessel N. van Wieringen
      Abstract: A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices through ridge (ℓ2) penalties. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-ℓ2-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer's disease.
      PubDate: Mon, 02 May 2022 00:00:00 +000
       
  • Event History Regression with Pseudo-Observations: Computational
           Approaches and an Implementation in R

    • Authors: Michael C. Sachs; Erin E. Gabriel
      Abstract: Due to tradition and ease of estimation, the vast majority of clinical and epidemiological papers with time-to-event data report hazard ratios from Cox proportional hazards regression models. Although hazard ratios are well known, they can be difficult to interpret, particularly as causal contrasts, in many settings. Nonparametric or fully parametric estimators allow for the direct estimation of more easily causally interpretable estimands such as the cumulative incidence and restricted mean survival. However, modeling these quantities as functions of covariates is limited to a few categorical covariates with nonparametric estimators, and often requires simulation or numeric integration with parametric estimators. Combining pseudo-observations based on non-parametric estimands with parametric regression on the pseudo-observations allows for the best of these two approaches and has many nice properties. In this paper, we develop a user friendly, easy to understand way of doing event history regression for the cumulative incidence and the restricted mean survival, using the pseudo-observation framework for estimation. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and correct variance estimation.
      PubDate: Mon, 02 May 2022 00:00:00 +000
       
  • GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language

    • Authors: Jamie Fairbrother; Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder
      Abstract: Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this paper, we present a tutorial of the GaussianProcesses.jl package that has been developed for the Julia programming language. GaussianProcesses.jl utilizes the inherent computational benefits of the Julia language, including multiple dispatch and just-in-time compilation, to produce a fast, flexible and user-friendly Gaussian processes package. The package provides many mean and kernel functions with supporting inference tools to fit exact Gaussian process models, as well as a range of alternative likelihood functions to handle non-Gaussian data (e.g., binary classification models) and sparse approximations for scalable Gaussian processes. The package makes efficient use of existing Julia packages to provide users with a range of optimization and plotting tools.
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
  • Multivariate Normal Variance Mixtures in R: The R Package nvmix

    • Authors: Erik Hintz; Marius Hofert, Christiane Lemieux
      Abstract: We present the features and implementation of the R package nvmix for the class of normal variance mixtures including Student t and normal distributions. The package provides functionalities for such distributions, notably the evaluation of the distribution and density function as well as likelihood-based parameter estimation. The distributional family is specified through the quantile function of the underlying mixing random variable. The R package nvmix thus allows one to model multivariate distributions well beyond the classical multivariate normal and t case. Additional functionalities include graphical goodness-of-fit assessment, the estimation of the risk measures value-at-risk and expected shortfall for univariate normal variance mixture distributions and functions to work with normal variance mixture copulas, such as sampling and the evaluation of normal variance mixture copulas and their densities. Furthermore, the package nvmix also provides functionalities for the evaluation of the distribution and density function as well as random variate generation for the more general class of grouped normal variance mixtures.
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
  • sensobol: An R Package to Compute Variance-Based Sensitivity Indices

    • Authors: Arnald Puy; Samuele Lo Piano, Andrea Saltelli, Simon A. Levin
      Abstract: The R package sensobol provides several functions to conduct variance-based uncertainty and sensitivity analysis, from the estimation of sensitivity indices to the visual representation of the results. It implements several state-of-the-art first and total-order estimators and allows the computation of up to fourth-order effects, as well as of the approximation error, in a swift and user-friendly way. Its flexibility makes it also appropriate for models with either a scalar or a multivariate output. We illustrate its functionality by conducting a variance-based sensitivity analysis of three classic models: the Sobol' (1998) G function, the logistic population growth model of Verhulst (1845), and the spruce budworm and forest model of Ludwig, Jones, and Holling (1976).
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
  • The R Package stagedtrees for Structural Learning of Stratified Staged
           Trees

    • Authors: Federico Carli; Manuele Leonelli, Eva Riccomagno, Gherardo Varando
      Abstract: stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorithms are implemented, as well as various functionalities to plot the models and perform inference. The capabilities of stagedtrees are illustrated using mainly two datasets both included in the package or bundled in R.
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
  • NeuralSens: Sensitivity Analysis of Neural Networks

    • Authors: Jaime Pizarroso; José Portela, Antonio Muñoz
      Abstract: This article presents the NeuralSens package that can be used to perform sensitivity analysis of neural networks using the partial derivatives method. The main function of the package calculates the partial derivatives of the output with regard to the input variables of a multi-layer perceptron model, which can be used to evaluate variable importance based on sensitivity measures and characterize relationships between input and output variables. Methods to calculate partial derivatives are provided for objects trained using common neural network packages in R, and a 'numeric' method is provided for objects from packages which are not included. The package also includes functions to plot the information obtained from the sensitivity analysis. The article contains an overview of techniques for obtaining information from neural network models, a theoretical foundation of how partial derivatives are calculated, a description of the package functions, and applied examples to compare NeuralSens functions with analogous functions from other available R packages.
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
  • econet: An R Package for Parameter-Dependent Network Centrality Measures

    • Authors: Marco Battaglini; Valerio Leone Sciabolazza, Eleonora Patacchini, Sida Peng
      Abstract: The R package econet provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the econet package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Leone Sciabolazza, and Patacchini (2020).
      PubDate: Sat, 30 Apr 2022 00:00:00 +000
       
 
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