COMPUTATIONAL STATISTICS & DATA ANALYSIS计算统计与数据分析
COMPUTATIONAL STATISTICS & DATA ANALYSIS(英文缩写 COMPUT STAT DATA AN),ISSN 0167-9473,eISSN 1872-7352,中文译名:计算统计与数据分析 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 2.035 | Q2 |
| 2022 | 1.800 | Q2 |
| 2023 | 1.500 | Q2 |
| 2024 | 1.600 | Q2 |
| 2025 | 1.700 | Q2 |
COMPUTATIONAL STATISTICS & DATA ANALYSIS 最新收录文献
-
1. Modeling Disease-specific Survival in Observational Studies with Missing Cause of Death: Leveraging Information from Clinical Trial Data.
PMID:日期:2026-06-01Modeling disease-specific survival (DSS) is essential for evaluating the risk of death due to the disease itself, offering valuable insights into disease progression, identifying high-risk subgroups, and informing treatment decisions. The National Cancer Database (NCDB), one of the largest cancer registries in the United States, has the potential to be a valuable resource for assessing DSS in the general cancer population. However, the NCDB does not record cause-of-death information due to practical limitations. In contrast, randomized cancer clinical trials provide detailed documentation of the cause of death. To bridge this gap, a framework is developed to leverage cause-of-death information from clinical trials to infer DSS in the NCDB population through a proportional cause ratio model. This model accommodates both temporal variation and patient-level characteristics influencing the distribution of causes of death. To account for population heterogeneity between trial participants and registry patients, information transfer is restricted to the cause-of-death mechanism rather than the overall survival distribution. Estimation procedures are established, and the asymptotic properties of the resulting estimators are rigorously derived. Extensive simulation studies demonstrate the validity of the proposed estimators and associated inference under both the proportional cause ratio model and the proportional hazards model for DSS. The proposed method is applied to our motivating study in which cause of death information from the National Surgical Adjuvant Breast and Bowel Project (NSABP B-06 trial) is transferred to the NCDB to infer breast cancer-specific survival based on demographics of the patient, clinical characteristics, and treatments received.
-
2. {"_":"Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-4.","i":["ε"]}
PMID:日期:2025-09-29Association analysis of multivariate omics outcomes is challenging due to the high dimensionality and inter-correlation among outcome variables. In practice, the classic multi-univariate analysis approaches are commonly employed, utilizing linear regression models for each individual outcome followed by adjustments for multiplicity through control of the false discovery rate (FDR) or family-wise error rate (FWER). While straightforward, these multi-univariate methods overlook dependencies between outcome variables. This oversight leads to less accurate statistical inferences, characterized by lower power and an increased false discovery rate, ultimately resulting in reduced replicability across studies. Recently, advanced frequentist and Bayesian methods have been developed to account for these dependencies. However, these methods often pose significant computational challenges for researchers in the field. To bridge this gap, a computationally efficient autoregressive multivariate regression model is proposed that explicitly accounts for the dependence structure among outcome variables. Through extensive simulations, it is demonstrated that the approach provides more accurate multivariate inferences than traditional methods and remains robust even under model misspecification. Additionally, the proposed method is applied to investigate whether the associations between serum lipidomics outcomes and Alzheimer's disease differentiate in 4 allele carriers and non-carriers of the apolipoprotein E (APOE) gene.
-
3. A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models.
PMID:日期:2025-04-23A method is demonstrated for localizing where two spline terms, or smooths, differ using a true discovery proportion (TDP)-based interpretation. The procedure yields a statement on the proportion of some region where true differences exist between two smooths. The methodology avoids ad hoc approaches to making such statements, like subsetting the data and performing hypothesis tests on the truncated spline terms. TDP estimates are confidence-bounded simultaneously, which means that a region's TDP estimate is a lower bound on the proportion of actual differences, or true discoveries, in that region, with high confidence regardless of the number of estimates made. The procedure is based on closed-testing using Simes local test. This local test requires that the multivariate test statistics of generalized Wishart type underlying the method be positive regression dependent on subsets (PRDS), a result for which evidence is presented suggesting that the condition holds. Consistency of the procedure is demonstrated for generalized additive models with the tuning parameter chosen by REML or GCV, and the achievement of confidence-bounded TDP is shown in simulation as is an analysis of walking gait.
-
4. Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves.
PMID:日期:2025-03The analysis of multivariate functional curves has the potential to yield important scientific discoveries in domains such as healthcare, medicine, economics and social sciences. However, it is common for real-world settings to present longitudinal data that are both irregularly and sparsely observed, which introduces important challenges for the current functional data methodology. A Bayesian hierarchical framework for multivariate functional principal component analysis is proposed, which accommodates the intricacies of such irregular observation settings by flexibly pooling information across subjects and correlated curves. The model represents common latent dynamics via shared functional principal component scores, thereby effectively borrowing strength across curves while circumventing the computationally challenging task of estimating covariance matrices. These scores also provide a parsimonious representation of the major modes of joint variation of the curves and constitute interpretable scalar summaries that can be employed in follow-up analyses. Estimation is conducted using variational inference, ensuring that accurate posterior approximation and robust uncertainty quantification are achieved. The algorithm also introduces a novel variational message passing fragment for multivariate functional principal component Gaussian likelihood that enables modularity and reuse across models. Detailed simulations assess the effectiveness of the approach in sharing information from sparse and irregularly sampled multivariate curves. The methodology is also exploited to estimate the molecular disease courses of individual patients with SARS-CoV-2 infection and characterise patient heterogeneity in recovery outcomes; this study reveals key coordinated dynamics across the immune, inflammatory and metabolic systems, which are associated with long-COVID symptoms up to one year post disease onset. The approach is implemented in the R package bayesFPCA.
-
5. Sparse vertex discriminant analysis: Variable selection for biomedical classification applications.
PMID:日期:2025-01-07Modern biomedical datasets are often high-dimensional at multiple levels of biological organization. Practitioners must therefore grapple with data to estimate sparse or low-rank structures so as to adhere to the principle of parsimony. Further complicating matters is the presence of groups in data, each of which may have distinct associations with explanatory variables or be characterized by fundamentally different covariates. These themes in data analysis are explored in the context of classification. Vertex Discriminant Analysis (VDA) offers flexible linear and nonlinear models for classification that generalize the advantages of support vector machines to data with multiple classes. The proximal distance principle, which leverages projection and proximal operators in the design of practical algorithms, handily facilitates variable selection in VDA via nonconvex distance-to-set penalties directly controlling the number of active variables. Two flavors of sparse VDA are developed to address data in which instances may be homogeneous or heterogeneous with respect to predictors characterizing classes. Empirical studies illustrate how VDA is adapted to class-specific variable selection on simulated and real datasets, with an emphasis on applications to cancer classification via gene expression patterns.
-
6. A double Pólya-Gamma data augmentation scheme for a hierarchical Negative Binomial - Binomial data model.
PMID:日期:2024-06-26A double Pólya-Gamma data augmentation scheme is developed for posterior sampling from a Bayesian hierarchical model of total and categorical count data. The scheme applies to a Negative Binomial - Binomial (NBB) hierarchical regression model with logit links and normal priors on regression coefficients. The approach is shown to be very efficient and in most cases out-performs the Stan program. The hierarchical modeling framework and the Pólya-Gamma data augmentation scheme are applied to human mitochondrial DNA data.
-
7. Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.
PMID:日期:2024-04-30Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.
-
8. Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.
PMID:日期:2024-03-24In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.
-
9. Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.
PMID:日期:2024-02-09Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.
-
10. {"_":"Estimation of Norm Penalized Models: A Statistical Treatment.","mml:math":[{"xmlns:mml":["http://www.w3.org/1998/Math/MathML"],"mml:mrow":[{"mml:msub":[{"mml:mi":["l"],"mml:mtext":["0"]}]}]}]}
PMID:日期:2023-12-06Fitting penalized models for the purpose of merging the estimation and model selection problem has become commonplace in statistical practice. Of the various regularization strategies that can be leveraged to this end, the use of the norm to penalize parameter estimation poses the most daunting model fitting task. In fact, this particular strategy requires an end user to solve a non-convex NP-hard optimization problem irregardless of the underlying data model. For this reason, the use of the norm as a regularization strategy has been woefully under utilized. To obviate this difficulty, a strategy to solve such problems that is generally accessible by the statistical community is developed. The approach can be adopted to solve norm penalized problems across a very broad class of models, can be implemented using existing software, and is computationally efficient. The performance of the method is demonstrated through in-depth numerical experiments and through using it to analyze several prototypical data sets.