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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 276)
Statistics in Medicine     Hybrid Journal   (Followers: 141)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 76, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 49)
Sociological Methods & Research     Hybrid Journal   (Followers: 48)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 25)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 17)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
International Statistical Review     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Stata Journal     Full-text available via subscription   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  

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Similar Journals
Journal Cover
Statistics and Computing
Journal Prestige (SJR): 2.545
Citation Impact (citeScore): 2
Number of Followers: 14  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-1375 - ISSN (Online) 0960-3174
Published by Springer-Verlag Homepage  [2468 journals]
  • Clustering longitudinal ordinal data via finite mixture of matrix-variate
           distributions

    • Free pre-print version: Loading...

      Abstract: Abstract In social sciences, studies are often based on questionnaires asking participants to express ordered responses several times over a study period. We present a model-based clustering algorithm for such longitudinal ordinal data. Assuming that an ordinal variable is the discretization of an underlying latent continuous variable, the model relies on a mixture of matrix-variate normal distributions, accounting simultaneously for within- and between-time dependence structures. The model is thus able to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure. An EM algorithm is developed and presented for parameters estimation, and approaches to deal with some arising computational challenges are outlined. An evaluation of the model through synthetic data shows its estimation abilities and its advantages when compared to competitors. A real-world application concerning changes in eating behaviors during the Covid-19 pandemic period in France will be presented.
      PubDate: 2024-02-17
       
  • The minimum covariance determinant estimator for interval-valued data

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      Abstract: Abstract Effective estimation of covariance matrices is crucial for statistical analyses and applications. In this paper, we focus on the robust estimation of covariance matrix for interval-valued data in low and moderately high dimensions. In the low-dimensional scenario, we extend the Minimum Covariance Determinant (MCD) estimator to interval-valued data. We derive an iterative algorithm for computing this estimator, demonstrate its convergence, and theoretically establish that it retains the high breakdown-point property of the MCD estimator. Further, we propose a projection-based estimator and a regularization-based estimator to extend the MCD estimator to moderately high-dimensional settings, respectively. We propose efficient iterative algorithms for solving these two estimators and demonstrate their convergence properties. We conduct extensive simulation studies and real data analysis to validate the finite sample properties of these proposed estimators.
      PubDate: 2024-02-17
       
  • Enmsp: an elastic-net multi-step screening procedure for high-dimensional
           regression

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      Abstract: Abstract To improve the estimation efficiency of high-dimensional regression problems, penalized regularization is routinely used. However, accurately estimating the model remains challenging, particularly in the presence of correlated effects, wherein irrelevant covariates exhibit strong correlation with relevant ones. This situation, referred to as correlated data, poses additional complexities for model estimation. In this paper, we propose the elastic-net multi-step screening procedure (EnMSP), an iterative algorithm designed to recover sparse linear models in the context of correlated data. EnMSP uses a small repeated penalty strategy to identify truly relevant covariates in a few iterations. Specifically, in each iteration, EnMSP enhances the adaptive lasso method by adding a weighted \(l_2\) penalty, which improves the selection of relevant covariates. The method is shown to select the true model and achieve the \(l_2\) -norm error bound under certain conditions. The effectiveness of EnMSP is demonstrated through numerical comparisons and applications in financial data.
      PubDate: 2024-02-16
       
  • Bayesian parameter inference for partially observed stochastic volterra
           equations

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      Abstract: Abstract In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. Richard et al. in Stoch Proc Appl 141:109–138, 2021) we use Euler–Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods (Jasra et al. in SIAM J Sci Comp 40:A887–A902, 2018) can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a mean square error (MSE) of \(\mathcal {O}(\epsilon ^2)\) , \(\epsilon >0\) , is \(\mathcal {O}(\epsilon ^{-\tfrac{4}{2H+1}})\) , where H is the Hurst parameter. If one uses a single level MCMC method then the cost is \(\mathcal {O}(\epsilon ^{-\tfrac{2(2H+3)}{2H+1}})\) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real data.
      PubDate: 2024-02-14
       
  • Subsampling approach for least squares fitting of semi-parametric
           accelerated failure time models to massive survival data

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      Abstract: Abstract Massive survival data are increasingly common in many research fields, and subsampling is a practical strategy for analyzing such data. Although optimal subsampling strategies have been developed for Cox models, little has been done for semiparametric accelerated failure time (AFT) models due to the challenges posed by non-smooth estimating functions for the regression coefficients. We develop optimal subsampling algorithms for fitting semi-parametric AFT models using the least-squares approach. By efficiently estimating the slope matrix of the non-smooth estimating functions using a resampling approach, we construct optimal subsampling probabilities for the observations. For feasible point and interval estimation of the unknown coefficients, we propose a two-step method, drawing multiple subsamples in the second stage to correct for overestimation of the variance in higher censoring scenarios. We validate the performance of our estimators through a simulation study that compares single and multiple subsampling methods and apply the methods to analyze the survival time of lymphoma patients in the Surveillance, Epidemiology, and End Results program.
      PubDate: 2024-02-14
       
  • New mixed portmanteau tests for time series models

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      Abstract: Abstract This article proposes omnibus portmanteau tests for contrasting adequacy of time series models. The test statistics are based on combining the autocorrelation function of the conditional residuals, the autocorrelation function of the conditional squared residuals, and the cross-correlation function between these residuals and their squares. The maximum likelihood estimator is used to derive the asymptotic distribution of the proposed test statistics under a general class of time series models, including ARMA, GARCH, and other nonlinear structures. An extensive Monte Carlo simulation study shows that the proposed tests successfully control the type I error probability and tend to have more power than other competitor tests in many scenarios. Two applications to a set of weekly stock returns for 92 companies from the S &P 500 demonstrate the practical use of the proposed tests.
      PubDate: 2024-02-12
       
  • COMBSS: best subset selection via continuous optimization

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      Abstract: Abstract The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly challenging when the total available number of features is very large compared to the number of data samples. Existing optimal methods for solving this problem tend to be slow while fast methods tend to have low accuracy. Ideally, new methods perform best subset selection faster than existing optimal methods but with comparable accuracy, or, being more accurate than methods of comparable computational speed. Here, we propose a novel continuous optimization method that identifies a subset solution path, a small set of models of varying size, that consists of candidates for the single best subset of features, that is optimal in a specific sense in linear regression. Our method turns out to be fast, making the best subset selection possible when the number of features is well in excess of thousands. Because of the outstanding overall performance, framing the best subset selection challenge as a continuous optimization problem opens new research directions for feature extraction for a large variety of regression models.
      PubDate: 2024-02-12
       
  • A robust quantile regression for bounded variables based on the
           Kumaraswamy Rectangular distribution

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      Abstract: Abstract Quantile regression (QR) models offer an interesting alternative compared with ordinary regression models for the response mean. Besides allowing a more appropriate characterization of the response distribution, the former is less sensitive to outlying observations than the latter. Indeed, the QR models allow modeling other characteristics of the response distribution, such as the lower and/or upper tails. However, in the presence of outlying observations, the estimates can still be affected. In this context, a robust quantile parametric regression model for bounded responses is developed, considering a new distribution, the Kumaraswamy Rectangular (KR) distribution. The KR model corresponds to a finite mixture structure similar to the Beta Rectangular distribution. That is, the KR distribution has heavier tails compared to the Kumaraswamy model. Indeed, we show that the correspondent KR quantile regression model is more robust and flexible than the usual Kumaraswamy one. Bayesian inference, which includes parameter estimation, model fit assessment, model comparison, and influence analysis, is developed through a hybrid-based MCMC approach. Since the quantile of the KR distribution is not analytically tractable, we consider the modeling of the conditional quantile based on a suitable data augmentation scheme. To link both quantiles in terms of a regression structure, a two-step estimation algorithm under a Bayesian approach is proposed to obtain the numerical approximation of the respective posterior distributions of the parameters of the regression structure for the KR quantile. Such an algorithm combines a Markov Chain Monte Carlo algorithm with the Ordinary Least Squares approach. Our proposal showed to be robust against outlying observations related to the response while keeping the estimation process simple without adding too much to the computational complexity. We showed the effectiveness of our estimation method with a simulation study, whereas two other studies showed some benefits of the proposed model in terms of robustness and flexibility. To exemplify the adequacy of our approach, under the presence of outlying observations, we analyzed two data sets regarding socio-economic indicators from Brazil and compared them with alternatives.
      PubDate: 2024-02-10
       
  • A reliable data-based smoothing parameter selection method for circular
           kernel estimation

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      Abstract: Abstract A new data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown quantities on an optimal smoothing parameter are replaced by suitable estimates. This paper provides a circular version of the well-known Sheather and Jones bandwidths (J R Stat Soc Ser B Stat Methodol 53(3):683–690, 1991. https://doi.org/10.1111/j.2517-6161.1991.tb01857.x), with direct and solve-the-equation plug-in rules. Theoretical support for our developments, related to the asymptotic mean squared error of the estimator of the density, its derivatives, and its functionals, for circular data, are provided. The proposed selectors are compared with previous data-based smoothing parameters for circular kernel density estimation. This paper also contributes to the study of the optimal kernel for circular data. An illustration of the proposed plug-in rules is also shown using real data on the time of car accidents.
      PubDate: 2024-02-07
       
  • Fast generation of exchangeable sequences of clusters data

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      Abstract: Abstract Recent advances in Bayesian models for random partitions have led to the formulation and exploration of Exchangeable Sequences of Clusters (ESC) models. Under ESC models, it is the cluster sizes that are exchangeable, rather than the observations themselves. This property is particularly useful for obtaining microclustering behavior, whereby cluster sizes grow sublinearly in the number of observations, as is common in applications such as record linkage, sparse networks and genomics. Unfortunately, the exchangeable clusters property comes at the cost of projectivity. As a consequence, in contrast to more traditional Dirichlet Process or Pitman–Yor process mixture models, samples a priori from ESC models cannot be easily obtained in a sequential fashion and instead require the use of rejection or importance sampling. In this work, drawing on connections between ESC models and discrete renewal theory, we obtain closed-form expressions for certain ESC models and develop faster methods for generating samples a priori from these models compared with the existing state of the art. In the process, we establish analytical expressions for the distribution of the number of clusters under ESC models, which was unknown prior to this work.
      PubDate: 2024-02-07
       
  • Large-scale unsupervised spatio-temporal semantic analysis of vast regions
           from satellite images sequences

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      Abstract: Abstract Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km \(^2\) region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.
      PubDate: 2024-02-05
       
  • Global–local shrinkage multivariate logit-beta priors for multiple
           response-type data

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      Abstract: Abstract Multiple-type outcomes are often encountered in many statistical applications, one may want to study the association between multiple responses and determine the covariates useful for prediction. However, literature on variable selection methods for multiple-type data is arguably underdeveloped. In this article, we develop a novel global–local shrinkage prior in multiple response-types settings, where the observed dataset consists of multiple response-types (e.g., continuous, count-valued, Bernoulli trials, etc.), by combining the perspectives of global–local shrinkage and the conjugate multivaraite distribution. One benefit of our model is that a transformation or a Gaussian approximation on the data is not needed to perform variable selection for multiple response-type data, and thus one can avoid computational difficulties and restrictions on the joint distribution of the responses. Another benefit is that it allows one to parsimoniously model cross-variable dependence. Specifically, our method uses basis functions with random effects, which can be presented as known covariates or pre-defined basis functions, to model dependence between responses and dependence can be detected by our proposed global–local shrinkage model with a sparsity-inducing model. We provide connections to the original horseshoe model and existing basis function models. An efficient block Gibbs sampler is developed, which is found to be effective in obtaining accurate estimates and variable selection results. We also provide a motivating analysis of public health and financial costs from natural disasters in the U.S. using data provided by the National Centers for Environmental Information.
      PubDate: 2024-02-03
       
  • Kent feature embedding for classification of compositional data with zeros

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      Abstract: Abstract Compositional data have posed challenges to current classification methods owing to the non-negative and unit-sum constraints, especially when a certain of the components are zeros. In this paper, we develop an effective classification method for multivariate compositional data with certain of the components equal to zero. Specifically, a Kent feature embedding technique is first proposed to transform compositional data and improve data quality. We then use support vector machine as the state-of-the-art machine learning model to build the classifier. The proposed method is proved to be effective through numerical simulations. Results on multiple real datasets, including species classification, day-night image classification and household’s consumption pattern recognition, further verify that the proposed method can achieve good classification performance and outperform the other competitors. This method would help to broaden the practical usage of compositional data with zeros in the task of classification.
      PubDate: 2024-01-31
       
  • The sparse dynamic factor model: a regularised quasi-maximum likelihood
           approach

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      Abstract: Abstract The concepts of sparsity, and regularised estimation, have proven useful in many high-dimensional statistical applications. Dynamic factor models (DFMs) provide a parsimonious approach to modelling high-dimensional time series, however, it is often hard to interpret the meaning of the latent factors. This paper formally introduces a class of sparse DFMs whereby the loading matrices are constrained to have few non-zero entries, thus increasing interpretability of factors. We present a regularised M-estimator for the model parameters, and construct an efficient expectation maximisation algorithm to enable estimation. Synthetic experiments demonstrate consistency in terms of estimating the loading structure, and superior predictive performance where a low-rank factor structure may be appropriate. The utility of the method is further illustrated in an application forecasting electricity consumption across a large set of smart meters.
      PubDate: 2024-01-22
       
  • Variational Bayesian analysis of survival data using a log-logistic
           accelerated failure time model

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      Abstract: Abstract The log-logistic regression model is one of the most commonly used accelerated failure time (AFT) models in survival analysis, for which statistical inference methods are mainly established under the frequentist framework. Recently, Bayesian inference for log-logistic AFT models using Markov chain Monte Carlo (MCMC) techniques has also been widely developed. In this work, we develop an alternative approach to MCMC methods and infer the parameters of the log-logistic AFT model via a mean-field variational Bayes (VB) algorithm. A piecewise approximation technique is embedded in deriving the VB algorithm to achieve conjugacy. The proposed VB algorithm is evaluated and compared with frequentist and MCMC techniques using simulated data under various scenarios. A publicly available dataset is employed for illustration. We have demonstrated that our proposed Variational Bayes (VB) algorithm consistently produces satisfactory estimation results and, in most scenarios, outperforms the likelihood-based method in terms of empirical mean squared error (MSE). When compared to MCMC, similar performance was achieved by our proposed VB, and, in certain scenarios, VB yielded the lowest MSE. Furthermore, the proposed VB algorithm offers a significantly reduced computational cost compared to MCMC, with an average speedup of 300 times.
      PubDate: 2024-01-18
       
  • Efficient exponential tilting with applications

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      Abstract: Abstract To minimize the variance of Monte Carlo estimators, we develop a novel exponential embedding technique that extends the classical concept of sufficient statistics in importance sampling. Our method demonstrates bounded relative error and logarithmic efficiency when applied to normal and gamma distributions, especially in rare event scenarios. To illustrate this innovative technique, we address the problem of credit risk measurement in portfolios and present an efficient simulation algorithm to estimate the likelihood of significant portfolio losses, leveraging multi-factor models with a normal mixture copula. Finally, supported by comprehensive simulation studies, our approach offers a more effective and efficient way to simulate moderately rare events.
      PubDate: 2024-01-13
       
  • Expectile hidden Markov regression models for analyzing cryptocurrency
           returns

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      Abstract: Abstract In this paper we develop a linear expectile hidden Markov model for the analysis of cryptocurrency time series in a risk management framework. The methodology proposed allows to focus on extreme returns and describe their temporal evolution by introducing in the model time-dependent coefficients evolving according to a latent discrete homogeneous Markov chain. As it is often used in the expectile literature, estimation of the model parameters is based on the asymmetric normal distribution. Maximum likelihood estimates are obtained via an Expectation–Maximization algorithm using efficient M-step update formulas for all parameters. We evaluate the introduced method with both artificial data under several experimental settings and real data investigating the relationship between daily Bitcoin returns and major world market indices.
      PubDate: 2024-01-13
       
  • Bayesian contiguity constrained clustering

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      Abstract: Abstract Clustering is a well-known and studied problem, one of its variants, called contiguity-constrained clustering, accepts as a second input a graph used to encode prior information about cluster structure by means of contiguity constraints i.e. clusters must form connected subgraphs of this graph. This paper discusses the interest of such a setting and proposes a new way to formalise it in a Bayesian setting, using results on spanning trees to compute exactly a posteriori probabilities of candidate partitions. An algorithmic solution is then investigated to find a maximum a posteriori partition and extract a Bayesian dendrogram from it. The interest of this last tool, which is reminiscent of the classical output of a simple hierarchical clustering algorithm, is analysed. Finally, the proposed approach is demonstrated with experiments on simulated data and real applications. A reference implementation of this work is available in the R package gtclust that accompanies the paper.
      PubDate: 2024-01-12
       
  • Nonparametric Bayesian online change point detection using kernel density
           estimation with nonparametric hazard function

    • Free pre-print version: Loading...

      Abstract: Abstract This paper aims to develop Bayesian online change point detection (BOCD), a parametric change point detection method, into a nonparametric method to be able to detect change points in a free-distribution time series. Instead of using predefined exponential family distribution for predictive probability, we use kernel density estimation in which two possible options have been proposed. The first is manual constant bandwidth selection. This option provides a fast computation of KDE as it can follow dynamic programming. Another option for the best performance is a nonparametric bandwidth estimator. For the goal of fully nonparametric change point detection, the predefined hazard function in the BOCD method is changed to be a nonparametric estimator. The performance of the proposed method is intensively evaluated with simulated data and compared with other traditional methods. It is found that nonparametric BOCD gives a better solution in all cases as a consequence of the adaptive property of KDE. Furthermore, the real-life application of the method with real data demonstrated its outstanding performance in detecting change points across diverse datasets. This success signifies a promising solution for expanding the potential of change point detection algorithms across a broader range of fields through the use of a nonparametric approach. Nevertheless, it requires a sufficient amount of data to form a precise predictive distribution curve to accurately detect change points.
      PubDate: 2024-01-12
       
  • Maximum likelihood estimation for discrete latent variable models via
           evolutionary algorithms

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      Abstract: Abstract We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation–maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.
      PubDate: 2024-01-10
       
 
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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 276)
Statistics in Medicine     Hybrid Journal   (Followers: 141)
Journal of Econometrics     Hybrid Journal   (Followers: 84)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 76, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 49)
Sociological Methods & Research     Hybrid Journal   (Followers: 48)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 42)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 41, SJR: 3.664, CiteScore: 2)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 35)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 30)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 25)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Forecasting     Hybrid Journal   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 18)
Journal of Statistical Software     Open Access   (Followers: 18, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 17)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Computational Statistics     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 11)
International Statistical Review     Hybrid Journal   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 9)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Stata Journal     Full-text available via subscription   (Followers: 9)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Research Synthesis Methods     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 7)
Biometrical Journal     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 6)
Significance     Hybrid Journal   (Followers: 6)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
Statistical Methods and Applications     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
TEST     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Sequential Analysis: Design Methods and Applications     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  

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