Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 10) Advances in Data Analysis and Classification       (Followers: 52) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 7) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 2) Australian & New Zealand Journal of Statistics       (Followers: 12) Biometrical Journal       (Followers: 9) Biometrics       (Followers: 51) British Journal of Mathematical and Statistical Psychology       (Followers: 17) Building Simulation       (Followers: 2) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 15) Computational Statistics & Data Analysis       (Followers: 35) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 12) Demographic Research       (Followers: 14) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 4) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 8) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 11) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 2) International Statistical Review       (Followers: 12) Journal of Algebraic Combinatorics       (Followers: 3) Journal of Applied Statistics       (Followers: 20) Journal of Biopharmaceutical Statistics       (Followers: 23) Journal of Business & Economic Statistics       (Followers: 38, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 21) Journal of Econometrics       (Followers: 82) Journal of Educational and Behavioral Statistics       (Followers: 7) Journal of Forecasting       (Followers: 19) Journal of Global Optimization       (Followers: 6) Journal of Mathematics and Statistics       (Followers: 6) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 34) Journal of Statistical and Econometric Methods       (Followers: 3) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 7) Journal of Statistical Software       (Followers: 16, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 36) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 28) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 41) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 23) Law, Probability and Risk       (Followers: 6) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 3) Multivariate Behavioral Research       (Followers: 8) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 6) Oxford Bulletin of Economics and Statistics       (Followers: 33) Pharmaceutical Statistics       (Followers: 16) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 7) Review of Economics and Statistics       (Followers: 138) Review of Socionetwork Strategies Risk Management       (Followers: 17) Sankhya A       (Followers: 3) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 40) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques Stata Journal       (Followers: 8) Statistica Neerlandica       (Followers: 1) Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 18) Statistical Papers       (Followers: 4) Statistics & Probability Letters       (Followers: 13) Statistics and Computing       (Followers: 13) Statistics and Economics Statistics in Medicine       (Followers: 122) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 12) Stochastic Models       (Followers: 2) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 11) Teaching Statistics       (Followers: 8) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 2) The American Statistician       (Followers: 25) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 10) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Computational StatisticsJournal Prestige (SJR): 0.803 Citation Impact (citeScore): 1Number of Followers: 15      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1613-9658 - ISSN (Online) 0943-4062 Published by Springer-Verlag  [2469 journals]
• Semi-supervised adapted HMMs for P2P credit scoring systems with reject
inference

Abstract: The majority of current credit-scoring models, used for loan approval processing, are generally built on the basis of the information from the accepted credit applicants whose ability to repay the loan is known. This situation generates what is called the selection bias, presented by a sample that is not representative of the population of applicants, since rejected applications are excluded. Thus, the impact on the eligibility of those models from a statistical and economic point of view. Especially for the models used in the peer-to-peer lending platforms, since their rejection rate is extremely high. The method of inferring rejected applicants information in the process of construction of the credit scoring models is known as reject inference. This study proposes a semi-supervised learning framework based on hidden Markov models (SSHMM), as a novel method of reject inference. Real data from the Lending Club platform, the most used online lending marketplace in the United States as well as the rest of the world, is used to experiment the effectiveness of our method over existing approaches. The results of this study clearly illustrate the proposed method’s superiority, stability, and adaptability.
PubDate: 2022-05-14

• Using reference models in variable selection

Abstract: Variable selection, or more generally, model reduction is an important aspect of the statistical workflow aiming to provide insights from data. In this paper, we discuss and demonstrate the benefits of using a reference model in variable selection. A reference model acts as a noise-filter on the target variable by modeling its data generating mechanism. As a result, using the reference model predictions in the model selection procedure reduces the variability and improves stability, leading to improved model selection performance. Assuming that a Bayesian reference model describes the true distribution of future data well, the theoretically preferred usage of the reference model is to project its predictive distribution to a reduced model, leading to projection predictive variable selection approach. We analyse how much the great performance of the projection predictive variable is due to the use of reference model and show that other variable selection methods can also be greatly improved by using the reference model as target instead of the original data. In several numerical experiments, we investigate the performance of the projective prediction approach as well as alternative variable selection methods with and without reference models. Our results indicate that the use of reference models generally translates into better and more stable variable selection.
PubDate: 2022-05-14

• Predictors with measurement error in mixtures of polynomial regressions

Abstract: There has been a substantial body of research on mixtures-of-regressions models that has developed over the past 20 years. While much of the recent literature has focused on flexible mixtures-of-regressions models, there is still considerable utility for imposing structure on the mixture components through fully parametric models. One feature of the data that is scantly addressed in mixtures of regressions is the presence of measurement error in the predictors. The limited existing research on this topic concerns the case where classical measurement error is added to the classic mixtures-of-linear-regressions model. In this paper, we consider the setting of mixtures of polynomial regressions where the predictors are subject to classical measurement error. Moreover, each component is allowed to have a different degree for the polynomial structure. We utilize a generalized expectation-maximization algorithm for performing maximum likelihood estimation. For estimating standard errors, we extend a semiparametric bootstrap routine that has been employed for mixtures of linear regressions without measurement error in the predictors. Numeric work, for practical reasons identified, is limited to estimating two-component models. We consider a likelihood ratio test for determining if there is a higher-degree polynomial term in one of the components. Model selection criteria are also highlighted as a way for determining an appropriate model. A simulation study and an application to the classic nitric oxide emissions data are provided.
PubDate: 2022-05-13

• Model selection using PRESS statistic

Abstract: Abstract The most popularly used statistic $$R^2$$ has a fundamental weakness in model building: it favors adding more predictors to the model because $$R^2$$ can only increase. In effect, additional predictors start fitting noise to the data. Other measures used in selecting a regression model such as $$R^2_{adj}$$ , AIC, SBC, and Mallow’s $$C_p$$ does not guarantee that the model selected will also make better prediction of future values. To avoid this, data scientists withhold a percentage of the data for validation purposes. The PRESS statistic does something similar by withholding each observation in calculating its own predicted value. In this paper, we investigated the behavior of $$R^2_{PRESS}$$ , and how it performs compared to other criterion in model selection in the presence of unnecessary predictors. Using simulated data, we found $$R^2_{PRESS}$$ has generally performed best in selecting the true model as the best model for prediction among the model selection measures considered.
PubDate: 2022-05-03

• Speeding up the convergence of the alternating least squares algorithm
using vector $$\varepsilon$$ ε acceleration and restarting for nonlinear
principal component analysis

Abstract: Abstract Principal component analysis (PCA) is a widely used descriptive multivariate technique in the analysis of quantitative data. When applying PCA to mixed quantitative and qualitative data, we utilize an optimal scaling technique for quantifying qualitative data. PCA with optimal scaling is called nonlinear PCA. The alternating least squares (ALS) algorithm is used for computing nonlinear PCA. The ALS algorithm is stable in convergence and simple in implementation; however, the algorithm tends to converge slowly for large data matrices owing to its linear convergence. Then the v $$\varepsilon$$ -ALS algorithm, which incorporates the vector $$\varepsilon$$ accelerator into the ALS algorithm, is used to accelerate the convergence of the ALS algorithm for nonlinear PCA. In this paper, we improve the v $$\varepsilon$$ -ALS algorithm via a restarting procedure and further reduce its number of iterations and computation time. The restarting procedure is performed under simple restarting conditions, and it speeds up the convergence of the v $$\varepsilon$$ -ALS algorithm. The v $$\varepsilon$$ -ALS algorithm with a restarting procedure is referred to as the v $$\varepsilon$$ R-ALS algorithm. Numerical experiments examine the performance of the v $$\varepsilon$$ R-ALS algorithm by comparing its number of iterations and computation time with those of the ALS and v $$\varepsilon$$ -ALS algorithms.
PubDate: 2022-05-03

• Adaptive smoothing spline estimator for the function-on-function linear
regression model

PubDate: 2022-04-29

• Kernel regression for cause-specific hazard models with time-dependent
coefficients

Abstract: Abstract Competing risk data appear widely in modern biomedical research. In the past two decades, cause-specific hazard models are often used to deal with competing risk data. There is no current study on the kernel likelihood method for the cause-specific hazard model with time-varying coefficients. We propose to use the local partial log-likelihood approach for nonparametric time-varying coefficient estimation. Simulation studies demonstrate that our proposed nonparametric kernel estimator performs well under assumed finite sample settings. And we also compare the local kernel estimator with the penalized spline estimator. Finally, we apply the proposed method to analyze a diabetes dialysis study with competing death causes.
PubDate: 2022-04-29

• Approximate Bayesian computation using asymptotically normal point
estimates

Abstract: Abstract Approximate Bayesian computation (ABC) provides inference of the posterior distribution, even for models with intractable likelihoods, by replacing the exact (intractable) model likelihood by a tractable approximate likelihood. Meanwhile, historically, the development of point-estimation methods usually precedes the development of posterior estimation methods. We propose and study new ABC methods based on asymptotically normal and consistent point-estimators of the model parameters. Specifically, for the classical ABC method, we propose and study two alternative bootstrap methods for estimating the tolerance tuning parameter, based on resampling from the asymptotic normal distribution of the given point-estimator. This tolerance estimator can be quickly computed even for any model for which it is computationally costly to sample directly from its exact likelihood, provided that its summary statistic is specified as consistent point-estimator of the model parameters with estimated asymptotic normal distribution that can typically be easily sampled from. Furthermore, this paper introduces and studies a new ABC method based on approximating the exact intractable likelihood by the asymptotic normal density of the point-estimator, motivated by the Bernstein-Von Mises theorem. Unlike the classical ABC method, this new approach does not require tuning parameters, aside from the summary statistic (the parameter point estimate). Each of the new ABC methods is illustrated and compared through a simulation study of tractable models and intractable likelihood models, and through the Bayesian intractable likelihood analysis of a real 23,000-node network dataset involving stochastic search model selection.
PubDate: 2022-04-27

• The fuzzy cluster analysis for interval value using genetic algorithm and
its application in image recognition

Abstract: Abstract This article proposes the genetic algorithm in fuzzy clustering problem for interval value (IGI). In this algorithm, we use the overlap divergence to assess the similarity of the intervals, and take the new index (IDB) as the objective function to build the IGI. The crossover and selection operators in IGI are modified to optimize the results in clustering. The IGI not only determines the suitable number of groups, optimizes the result of clustering but also finds the probability of assigning the elements to the established clusters. The proposed algorithm is also applied in image recognition. The convergence of the IGI is considered and illustrated by the numerical examples. The complex computations of the IGI are performed conveniently and efficiently by the built Matlab program. The experiments on the data-sets having different characteristics and elements show the reasonableness of the IGI, and its advantages overcome other algorithms.
PubDate: 2022-04-26

• An efficient GPU-parallel coordinate descent algorithm for sparse
precision matrix estimation via scaled lasso

Abstract: Abstract The sparse precision matrix plays an essential role in the Gaussian graphical model since a zero off-diagonal element indicates conditional independence of the corresponding two variables given others. In the Gaussian graphical model, many methods have been proposed, and their theoretical properties are given as well. Among these, the sparse precision matrix estimation via scaled lasso (SPMESL) has an attractive feature in which the penalty level is automatically set to achieve the optimal convergence rate under the sparsity and invertibility conditions. Conversely, other methods need to be used in searching for the optimal tuning parameter. Despite such an advantage, the SPMESL has not been widely used due to its expensive computational cost. In this paper, we develop a GPU-parallel coordinate descent (CD) algorithm for the SPMESL and numerically show that the proposed algorithm is much faster than the least angle regression (LARS) tailored to the SPMESL. Several comprehensive numerical studies are conducted to investigate the scalability of the proposed algorithm and the estimation performance of the SPMESL. The results show that the SPMESL has the lowest false discovery rate for all cases and the best performance in the case where the level of the sparsity of the columns is high.
PubDate: 2022-04-16

• Results and student perspectives on a web-scraping assignment from Utah
State University’s data technologies course to evaluate the African
activity in the statistical computing community

Abstract: Abstract In 2019, members of the Executive Committee of the International Association for Statistical Computing (IASC) were contacted by members of the IASC from Africa asking whether it would be feasible to establish a new regional IASC section in Africa. The establishment of a new regional section requires several steps that are outlined in the IASC Statutes at https://iasc-isi.org/statutes/. The approval likely depends on whether the proposed new regional section has the potential to conduct typical section activities, such as organizing regional conferences, workshops, and short courses. To establish whether it is feasible to add a regional section in Africa, the IASC must know whether there is currently enough high-level activity within African countries with respect to computational statistics. To answer this question, we looked at author affiliations of articles published in the Springer journal Computational Statistics (COST) and the Elsevier journal Computational Statistics & Data Analysis (CSDA) from 2015 to 2020 and used these data as a proxy to compare author productivity for authors with an affiliation in Africa in 2019 and 2020, compared to authors with an affiliation in Latin America in 2015 and 2016. This article looks at quantitative results to the questions above, provides insight on how students from Utah State University’s STAT 5080/6080 “Data Technologies” course from the Fall 2019 semester used web scraping techniques in a homework assignment to gather author affiliations from COST and CSDA to answer these questions, and includes the evaluation of student feedback obtained after the end of the course.
PubDate: 2022-04-16

• Simplified partially observed quasi-information matrix

Abstract: Abstract We propose a simplified version of the partially observed quasi-information matrix (Poquim) method for inference about non-Gaussian linear mixed models and show its computational advantage over the original method. We illustrate the difference, and compare performance of the simplified version with Poquim as well as the normality-based method in simulation studies. An example of real-data analysis is considered.
PubDate: 2022-04-11

• Sparse reduced-rank regression for simultaneous rank and variable
selection via manifold optimization

Abstract: Abstract We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this issue, we develop an estimation algorithm with rank and variable selection via sparse regularization and manifold optimization, which enables us to obtain an accurate estimation of the coefficient parameter even if the true rank of the coefficient parameter is high. Using sparse regularization, we can also select an optimal value of the rank. We conduct Monte Carlo experiments and a real data analysis to illustrate the effectiveness of our proposed method.
PubDate: 2022-04-04

• Efficient computation of tight approximations to Chernoff bounds

Abstract: Abstract Chernoff bounds are a powerful application of the Markov inequality to produce strong bounds on the tails of probability distributions. They are often used to bound the tail probabilities of sums of Poisson trials, or in regression to produce conservative confidence intervals for the parameters of such trials. The bounds provide expressions for the tail probabilities that can be inverted for a given probability/confidence to provide tail intervals. The inversions involve the solution of transcendental equations and it is often convenient to substitute approximations that can be exactly solved e.g. by the quadratic equation. In this paper we introduce approximations for the Chernoff bounds whose inversion can be exactly solved with a quadratic equation, but which are closer approximations than those adopted previously.
PubDate: 2022-04-04

• Smallest covering regions and highest density regions for discrete
distributions

Abstract: Abstract This paper examines the problem of computing a canonical smallest covering region for an arbitrary discrete probability distribution. This optimisation problem is similar to the classical 0–1 knapsack problem, but it involves optimisation over a set that may be countably infinite, raising a computational challenge that makes the problem non-trivial. To solve the problem we present theorems giving useful conditions for an optimising region and we develop an iterative one-at-a-time computational method to compute a canonical smallest covering region. We show how this can be programmed in pseudo-code and we examine the performance of our method. We compare this algorithm with other algorithms available in statistical computation packages to compute HDRs. We find that our method is the only one that accurately computes HDRs for arbitrary discrete distributions.
PubDate: 2022-04-04

• Stochastic functional linear models and Malliavin calculus

Abstract: Abstract In this article, we study stochastic functional linear models (SFLM) driven by an underlying square integrable stochastic process X(t) which is generated by a standard Brownian motion. Utilizing the magnificent Itô integrals and Malliavin calculus, X(t) is expanded into a summation of orthogonal multiple integrals, i.e., Wiener-Itô chaos expansions, which is the counterpart of the Taylor expansion of deterministic functions. Based on the expansion, we show that the fourth moments of linear functionals of underlying stochastic process X(t) are bounded by the square of their second moments when X(t) is a finite linear combination of multiple Itô integrals. Therefore, an optimal minimax convergence rate in mean prediction risk of SFLM is valid if eigenvalues of related linear operators are of order $$k^{-2r}$$ by using results in literature when the underlying process X(t) is a linear combination of multiple Itô integrals. A sufficient and necessary condition of finite fourth moment of random functions of multiple Itô integrals is proved, which is a key condition in methodology and convergence rates of functional linear regressions. Our results show that the optimal minimax convergence rate in mean prediction risk can be applied to the class of linear combination of multiple Itô integrals which are not necessarily Gaussian processes. Moreover, the sufficient and necessary condition of finite fourth moment for multiple Itô integrals can be directly applied to show methodology and convergence rates of functional linear models. Using the theory of stochastic analysis, one may construct a reproducing kernel Hilbert space (RKHS) associated with a square integrable stochastic process to facilitate analysis of functional data.
PubDate: 2022-04-01

• Bayesian tests of symmetry for the generalized Von Mises distribution

Abstract: Abstract Bayesian tests on the symmetry of the generalized von Mises model for planar directions (Gatto and Jammalamadaka in Stat Methodol 4(3):341–353, 2007) are introduced. The generalized von Mises distribution is a flexible model that can be axially symmetric or asymmetric, unimodal or bimodal. A characterization of axial symmetry is provided and taken as null hypothesis for one of the proposed Bayesian tests. The Bayesian tests are obtained by the technique of probability perturbation. The prior probability measure is perturbed so to give a positive prior probability to the null hypothesis, which would be null otherwise. This allows for the derivation of simple computational formulae for the Bayes factors. Numerical results reveal that, whenever the simulation scheme of the samples supports the null hypothesis, the null posterior probabilities appear systematically larger than their prior counterpart.
PubDate: 2022-04-01

• A Hard EM algorithm for prediction of the cured fraction in survival data

Abstract: Abstract In clinical studies, survival analysis is a well known technique to analyze time to event data with the assumption that every subject in the study will encounter the event of interest. With recent advancements in the drug development industry, a fraction of subjects may not face the event and are considered as immune or cured. However, due to the finite study period, full knowledge of subjects who are immune is usually not known and hence, can be considered as missing. We develop a novel semi-parametric algorithm to address this problem by minimizing a suitable loss function, which incorporates the missing data and generates cure indicators for the censored individuals. We prove the existence of a global minimizer for the loss function and establish some asymptotic properties, demonstrate via numerical experiments that under appropriate circumstances, our approach performs better than simpler alternatives, and use this algorithm to estimate lifetime parameters and the overall survivor function.
PubDate: 2022-04-01

• Modelling the association in bivariate survival data by using a Bernstein
copula

Abstract: Abstract Bivariate or multivariate survival data arise when a sample consists of clusters of two or more subjects which are correlated. This paper considers clustered bivariate survival data which is possibly censored. Two approaches are commonly used in modelling such type of correlated data: random effect models and marginal models. A random effect model includes a frailty model and assumes that subjects are independent within a cluster conditionally on a common non-negative random variable, the so-called frailty. In contrast, the marginal approach models the marginal distribution directly and then imposes a dependency structure through copula functions. In this manuscript, Bernstein copulas are used to account for the correlation in modelling bivariate survival data. A two-stage parametric estimation method is developed to estimate in the first stage the parameters in the marginal models and in the second stage the coefficients of the Bernstein polynomials in the association. Hereby we use a penalty parameter to make the fit desirably smooth. In this aspect linear constraints are introduced to ensure uniform univariate margins and we use quadratic programming to fit the model. We perform a Simulation study and illustrate the method on a real data set.
PubDate: 2022-04-01

• Computing highest density regions for continuous univariate distributions
with known probability functions

Abstract: Abstract We examine the problem of computing the highest density region (HDR) in a computational context where the user has access to a density function and quantile function for the distribution (e.g., in the statistical language R). We examine several common classes of continuous univariate distributions based on the shape of the density function; this includes monotone densities, quasi-concave and quasi-convex densities, and general multimodal densities. In each case we show how the user can compute the HDR from the quantile and density functions by framing the problem as a nonlinear optimisation problem. We implement these methods in R to obtain general functions to compute HDRs for classes of distributions, and for commonly used families of distributions. We compare our method to existing R packages for computing HDRs and we show that our method performs favourably in terms of both accuracy and average speed.
PubDate: 2022-04-01

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