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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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Journal of the Korean Statistical Society
Journal Prestige (SJR): 0.545
Citation Impact (citeScore): 1
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1226-3192 - ISSN (Online) 2005-2863
Published by Elsevier Homepage  [2974 journals]
  • Jackknife model averaging for linear regression models with missing
           responses

    • Free pre-print version: Loading...

      Abstract: Abstract We consider model averaging estimation problem in the linear regression model with missing response data, that allows for model misspecification. Based on the ‘complete’ data set for the response variable after inverse propensity score weighted imputation, we construct a leave-one-out cross-validation criterion for allocating model weights, where the propensity score model is estimated by the covariate balancing propensity score method. We derive some theoretical results to justify the proposed strategy. Firstly, when all candidate outcome regression models are misspecified, our procedures are proved to achieve optimality in terms of asymptotically minimizing the squared loss. Secondly, when the true outcome regression model is among the set of candidate models, the resulting model averaging estimators of the regression parameters are shown to be root-n consistent. Simulation studies provide evidence of the superiority of our methods over other existing model averaging methods, even when the propensity score model is misspecified. As an illustration, the approach is further applied to study the CD4 data.
      PubDate: 2024-02-19
       
  • A self-normalization test for structural breaks in a regression model for
           panel data sets

    • Free pre-print version: Loading...

      Abstract: Abstract We construct a new structural break test in a panel regression model using the self-normalization method. The self-normalization test is shown to be superior to an existing test in that the former is theoretically and experimentally valid for regression models with serially and/or cross-sectionally correlated errors while the latter is not. We derive the asymptotic null distribution of the self-normalization test and its consistency under an alternative hypothesis. Unlike the existing test requiring bootstrap computation for critical values, the self-normalization test is implemented easily with a set of simple critical values. A Monte Carlo experiment reports that the self-normalization resolves the severe over-size problem of the existing test under serial and/or cross-sectional error correlation.
      PubDate: 2024-02-15
       
  • Gradient-based kernel variable selection for support vector hazards
           machine

    • Free pre-print version: Loading...

      Abstract: Abstract This study aims to improve the predictive performance for the event time through the machine learning model and find informative variables in the time-to-event data, simultaneously. To address this issue, after regarding the time-to-event data as the dichotomized counting processes data for predicting survival time, we consider the time-dependent support vector machine (SVM) framework for the dichotomized counting process data, where the decision function in this framework consists of the time-independent risk score and time-dependent intercept. Also, we consider the empirical partial derivative of the risk score function with respect to each marginal predictor as the indicator for the important predictor. Through this approach, it is possible to predict survival time and find variables that affect on the survival time at the same time. Simulation studies were conducted to confirm the performance of the model, and real data analysis was conducted by predicting the survival time of the lung cancer after the diagnosis and selecting genes associate with lung cancer through human gene data.
      PubDate: 2024-02-15
       
  • Testing for conditional independence of survival time from covariate

    • Free pre-print version: Loading...

      Abstract: Abstract This study examined the test of independence of survival time from a covariate in a more general setting using empirical process techniques. Previous research has been extended in several ways: (1) allow incompleteness of observation owing to censoring (2) allow the time-dependent covariate (3) allow the non-uniform covariate (4) prove the validity of weighted bootstrap to implement the proposed testing procedure. Certain classes of test statistics that are functionals of a natural empirical process were studied, and the limiting distribution of these statistics was then derived using the functional delta method. The limiting distributions included some linear functionals of zero mean tight Brownian bridges under the null hypothesis, and the tests were consistent against general alternatives. Tests implemented using weighted bootstrap were shown to be valid. The proposals are illustrated via simulation studies and an application to acute leukemia data.
      PubDate: 2024-02-14
       
  • Asymptotic results of error density estimator in nonlinear autoregressive
           models

    • Free pre-print version: Loading...

      Abstract: Abstract This paper considers the asymptotic properties of the error density estimator in the nonlinear autoregressive models with \(\alpha\) -mixing errors. The asymptotic distribution and uniform convergence rate for the kernel density estimator of error density function are obtained. Last, some simulations of histograms, confidence intervals and mean integrated square errors are illustrated, which agree with our theoretical results.
      PubDate: 2024-02-13
       
  • Disseminating massive frequency tables by masking aggregated cell
           frequencies

    • Free pre-print version: Loading...

      Abstract: Abstract We propose a confidential approach for disseminating frequency tables constructed for any combination of key variables in the given microdata, including those of hierarchical key variables. The system generates all possible frequency tables by either marginalizing or aggregating fully joint frequency tables of key variables while protecting the original cells with low frequencies through two masking steps: the small cell adjustments for joint tables followed by the proposed algorithm called information loss bounded aggregation for aggregated cells. The two-step approach is designed to control both disclosure risk and information loss by ensuring the k-anonymity of original cells with small frequencies while keeping the loss within a bounded limit.
      PubDate: 2024-01-30
       
  • Use of ridge calibration method in predicting election results

    • Free pre-print version: Loading...

      Abstract: Abstract Ridge calibration is a penalized method used in survey sampling to reduce the variability of the final set of weights by relaxing the linear restrictions. We proposed a method for selecting the penalty parameter that minimizes the estimated mean squared error of the mean estimator when estimated auxiliary information is used. We showed that the proposed estimator is asymptotically equivalent to the generalized regression estimator. A simple simulation study shows that our estimator has the smaller MSE compared to the traditional calibration ones. We applied our method to predict election result using National Barometer Survey and Korea Social Integration Survey.
      PubDate: 2024-01-23
       
  • Asymptotic of the number of false change points of the fused lasso signal
           approximator

    • Free pre-print version: Loading...

      Abstract: Abstract It is well-known that the fused lasso signal approximator (FLSA) is inconsistent in change point detection under the presence of staircase blocks in true mean values. The existing studies focus on modifying the FLSA model to remedy this inconsistency. However, the inconsistency of the FLSA does not severely degrade the performance in change point detection if the FLSA can identify all true change points and the estimated change points set is sufficiently close to the true change points set. In this study, we investigate some asymptotic properties of the FLSA under the assumption of the noise level \(\sigma _n = o(n \log n)\) . To be specific, we show that all the falsely segmented blocks are sub-blocks of true staircase blocks if the noise level is sufficiently low and a tuning parameter is chosen appropriately. In addition, each false change point of the optimal FLSA estimate can be associated with a vertex of a concave majorant or a convex minorant of a discrete Brownian bridge. Based on these results, we derive an asymptotic distribution of the number of false change points and provide numerical examples supporting the theoretical results.
      PubDate: 2024-01-18
       
  • Large sample properties of maximum likelihood estimator using moving
           extremes ranked set sampling

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we investigate the maximum likelihood estimator (MLE) for the parameter \(\theta\) in the probability density function \(f(x;\theta )\) . We specifically focus on the application of moving extremes ranked set sampling (MERSS) and analyze its properties in large samples. We establish the existence and uniqueness of the MLE for two common distributions when utilizing MERSS. Our theoretical analysis demonstrates that the MLE obtained through MERSS is, at the very least, as efficient as the MLE obtained through simple random sampling with an equivalent sample size. To substantiate these theoretical findings, we conduct numerical experiments. Furthermore, we explore the implications of imperfect ranking and provide a practical illustration by applying our approach to a real dataset.
      PubDate: 2024-01-13
       
  • Logistic regression models for elastic shape of curves based on tangent
           representations

    • Free pre-print version: Loading...

      Abstract: Abstract Shape analysis is widely used in many application areas such as computer vision, medical and biological studies. One challenge to analyze the shape of an object in an image is its invariant property to shape-preserving transformations. To measure the distance or dissimilarity between two different shapes, we worked with the square-root velocity function (SRVF) representation and the elastic metric. Since shapes are inherently high-dimensional in a nonlinear space, we adopted a tangent space at the mean shape and a few principal components (PCs) on the linearized space. We proposed classification methods based on logistic regression using these PCs and tangent vectors with the elastic net penalty. We then compared its performance with other model-based methods for shape classification in application to shape of algae in watersheds as well as simulated data generated by the mixture of von Mises-Fisher distributions.
      PubDate: 2024-01-12
       
  • Byzantine-resilient decentralized network learning

    • Free pre-print version: Loading...

      Abstract: Abstract Decentralized federated learning based on fully normal nodes has drawn attention in modern statistical learning. However, due to data corruption, device malfunctioning, malicious attacks and some other unexpected behaviors, not all nodes can obey the estimation process and the existing decentralized federated learning methods may fail. An unknown number of abnormal nodes, called Byzantine nodes, arbitrarily deviate from their intended behaviors, send wrong messages to their neighbors and affect all honest nodes across the entire network through passing polluted messages. In this paper, we focus on decentralized federated learning in the presence of Byzantine attacks and then propose a unified Byzantine-resilient framework based on the network gradient descent and several robust aggregation rules. Theoretically, the convergence of the proposed algorithm is guaranteed under some weakly balanced conditions of network structure. The finite-sample performance is studied through simulations under different network topologies and various Byzantine attacks. An application to Communities and Crime Data is also presented.
      PubDate: 2024-01-10
       
  • Sequential online monitoring for autoregressive time series of counts

    • Free pre-print version: Loading...

      Abstract: Abstract This study considers the online monitoring problem for detecting the parameter change in time series of counts. For this task, we construct a monitoring process based on the residuals obtained from integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models. We consider this problem within a more general framework using martingale difference sequences as the monitoring problem on GARCH-type processes based on the residuals or score vectors can be viewed as a special case of the monitoring problems on martingale differences. The limiting behavior of the stopping rule is investigated in this general set-up and is applied to the INGARCH processes. To assess the performance of our method, we conduct Monte Carlo simulations. A real data analysis is also provided for illustration. Our findings in this empirical study demonstrate the validity of the proposed monitoring process.
      PubDate: 2024-01-02
       
  • Return prediction by machine learning for the Korean stock market

    • Free pre-print version: Loading...

      Abstract: Abstract In this study, we aim to forecast monthly stock returns and analyze factors influencing stock prices in the Korean stock market. To find a model that maximizes the cumulative return of the portfolio of stocks with high predicted returns, we use machine learning models such as linear models, tree-based models, neural networks, and learning to rank algorithms. We employ a novel validation metric which we call the Cumulative net Return of a Portfolio with top 10% predicted return (CRP10) for tuning hyperparameters to increase the cumulative return of the selected portfolio. CRP10 tends to provide higher cumulative returns compared to out-of-sample R-squared as a validation metric with the data that we used. Our findings indicate that Light Gradient Boosting Machine (LightGBM) and Gradient Boosted Regression Trees (GBRT) demonstrate better performance than other models when we apply a single model for the entire test period. We also take the strategy of changing the model on a yearly basis by assessing the best model annually and observed that it did not outperform the approach of using a single model such as LightGBM or GBRT for the entire period.
      PubDate: 2023-12-20
       
  • Spatially integrated estimator of finite population total by integrating
           data from two independent surveys using spatial information

    • Free pre-print version: Loading...

      Abstract: Abstract A major goal of survey sampling is finite population inference. In recent years, large-scale survey programs have encountered many practical challenges which include higher data collection cost, increasing non-response rate, increasing demand for disaggregated level statistics and desire for timely estimates. Data integration is a new field of research that provides a timely solution to these above-mentioned challenges by integrating data from multiple surveys. Now, it is possible to develop a framework that can efficiently combine information from several surveys to obtain more precise estimates of population parameters. In many surveys, parameters of interest are often spatial in nature, which means, the relationship between the study variable and covariates varies across all locations in the study area and this situation is referred as spatial non-stationarity. Hence, there is a need of a sampling methodology that can efficiently tackle this spatial non-stationarity problem and can be able to integrate this spatially referenced data to get more detailed information. In this study, a Geographically Weighted Spatially Integrated (GWSI) estimator of finite population total was developed by integrating data from two independent surveys using spatial information. The statistical properties of the proposed spatially integrated estimator were then evaluated empirically through a spatial simulation study. Three different spatial populations were generated having high spatial autocorrelation. The proposed spatially integrated estimator performed better than usual design-based estimator under all three populations. Furthermore, a Spatial Proportionate Bootstrap (SPB) method was developed for variance estimation of the proposed spatially integrated estimator.
      PubDate: 2023-12-19
       
  • Statistical integration of allele frequencies from several organizations

    • Free pre-print version: Loading...

      Abstract: Abstract Genetic evidence, especially evidence based on short tandem repeats, is of paramount importance for human identification in forensic inferences. In recent years, the identification of kinship using DNA evidence has drawn much attention in various fields. In particular, it is employed, using a criminal database, to confirm blood relations in forensics. The interpretation of the likelihood ratio when identifying an individual or a relationship depends on the allele frequencies that are used, and thus, it is crucial to obtain an accurate estimate of allele frequency. Each organization such as Supreme Prosecutors’ Office and Korean National Police Agency in Korea provides different statistical interpretations due to differing estimations of the allele frequency, which can lead to confusion in forensic identification. Therefore, it is very important to estimate allele frequency accurately, and doing so requires a certain amount of information. However, simply using a weighted average for each allele frequency may not be sufficient to determine biological independence. In this study, we propose a new statistical method for estimating allele frequency by integrating the data obtained from several organizations, and we analyze biological independence and differences in allele frequency relative to the weighted average of allele frequencies in various subgroups. Finally, our proposed method is illustrated using real data from 576 Korean individuals.
      PubDate: 2023-12-18
       
  • Classification of repeated measurements using bias corrected Euclidean
           distance discriminant function

    • Free pre-print version: Loading...

      Abstract: Abstract This paper introduces a novel approach for approximating misclassification probabilities in Euclidean distance classifier when the group means exhibit a bilinear structure such as in the growth curve model first proposed by Potthoff and Roy (Biometrika 51:313–326, 1964). Initially, by leveraging certain statistical relationships, we establish two general results for the improved Euclidean discriminant function in both weighted and unweighted growth curve mean structures. We derive these approximations for the expected misclassification probabilities with respect to the distribution of the improved Euclidean discriminant function. Additionally, we compare the misclassification probabilities of the improved Euclidean discriminant function, the standard Euclidean discriminant function, and the linear discriminant function. It is important to note that in cases where the mean structure is weighted, a higher number of repeated measurements yields better classification results with the improved Euclidean discriminant function and the standard Euclidean discriminant function, allowing for more information to be acquired, as opposed to the linear discriminant function, which performs well with a smaller number of repeated measurements. Furthermore, we evaluate the accuracy of the suggested approximations by Monte Carlo simulations.
      PubDate: 2023-12-12
       
  • Sparse functional linear models via calibrated concave-convex procedure

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we propose a calibrated ConCave-Convex Procedure (CCCP) for variable selection in high-dimensional functional linear models. The calibrated CCCP approach for the Smoothly Clipped Absolute Deviation (SCAD) penalty is known to produce a consistent solution path with probability converging to one in linear models. We incorporate the SCAD penalty into function-on-scalar regression models and phrase them as a type of group-penalized estimation using a basis expansion approach. We then implement the calibrated CCCP method to solve the nonconvex group-penalized problem. For the tuning procedure, we use the Extended Bayesian Information Criterion (EBIC) to ensure consistency in high-dimensional settings. In simulation studies, we compare the performance of the proposed method with two existing convex-penalized estimators in terms of variable selection consistency and prediction accuracy. Lastly, we apply the method to the gene expression dataset for sparsely estimating the time-varying effects of transcription factors on the regulation of yeast cell cycle genes.
      PubDate: 2023-12-03
       
  • Nonparametric longitudinal regression model to analyze shape data using
           the Procrustes rotation

    • Free pre-print version: Loading...

      Abstract: Abstract Shape, as an intrinsic concept, can be considered as a source of information in some statistical analysis contexts. For instance, one of the important topics in morphology is to study the shape changes along time. From a topological viewpoint, shape data are points on a particular manifold and so to construct a longitudinal model for treating shape variation is not as trivial as thought. Unlike using the common parametric models to do such a task, we invoke Procrustes analysis in the context of a nonparametric framework and propose a simple, yet useful, model to deal with shape changes. After conveying the problem into the nonparametric regression model, we utilize the weighted least squares method to estimates the related parameters. Also, we illustrate implementing this new model in simulation studies and analyzing two biological data sets. Our proposed model shows its superiority while compared with other counterpart models.
      PubDate: 2023-12-03
       
  • Normality test in random coefficient autoregressive models

    • Free pre-print version: Loading...

      Abstract: Abstract In this paper, we consider the problem of testing for normality of the two unobservable random processes included in the first order random coefficient autoregressive models. To this end, we propose an information matrix based test and derive its limiting null distribution. We conduct simulations to evaluate the performance and characteristics of the introduced test, and provide a real data analysis.
      PubDate: 2023-09-22
       
  • Mixture copulas with discrete margins and their application to imbalanced
           data

    • Free pre-print version: Loading...

      Abstract: Abstract This article introduces the approach of using Bayesian sampling to estimate the mixture copula with discrete margins, we further apply our models to solve the class imbalanced problems in data science by oversampling. The methodology makes it possible to learn and sample from the data set with the discrete and continuous features exists simultaneously. On the other hand, the discreetness of factors in a data set are not naturally considered for the classic SMOTE algorithm and classic random oversampling is simply performed by generating the already existing points, which do not give any new information to the classifiers and is easy to overfit. Copula methods enable us to generate new points with the correlation structure memorized by learning from the training set. Hence, the overfitting problems are reduced. Experiments with synthetic and real data are done in the article following the introduction of the methodology. The outcomes shows the validity of the approach when compared with the benchmark methods.
      PubDate: 2023-09-09
       
 
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              [Sort alphabetically]   [Restore default list]

  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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