Annals of Applied Statistics最新文献_第10页

MICROSIMULATION MODEL CALIBRATION USING INCREMENTAL MIXTURE APPROXIMATE BAYESIAN COMPUTATION. 使用增量混合近似贝叶斯计算的微观模拟模型校准。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-12-01 Epub Date: 2019-11-28 DOI: 10.1214/19-aoas1279

Carolyn M Rutter, Jonathan Ozik, Maria DeYoreo, Nicholson Collier

{"title":"MICROSIMULATION MODEL CALIBRATION USING INCREMENTAL MIXTURE APPROXIMATE BAYESIAN COMPUTATION.","authors":"Carolyn M Rutter, Jonathan Ozik, Maria DeYoreo, Nicholson Collier","doi":"10.1214/19-aoas1279","DOIUrl":"10.1214/19-aoas1279","url":null,"abstract":"Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration, which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 4","pages":"2189-2212"},"PeriodicalIF":1.8,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8534811/pdf/nihms-1656102.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39552777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

OBLIQUE RANDOM SURVIVAL FORESTS. 斜随机生存林

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2019-09-01 Epub Date: 2019-10-17 DOI: 10.1214/19-aoas1261

Byron C Jaeger, D Leann Long, Dustin M Long, Mario Sims, Jeff M Szychowski, Yuan-I Min, Leslie A Mcclure, George Howard, Noah Simon

{"title":"OBLIQUE RANDOM SURVIVAL FORESTS.","authors":"Byron C Jaeger, D Leann Long, Dustin M Long, Mario Sims, Jeff M Szychowski, Yuan-I Min, Leslie A Mcclure, George Howard, Noah Simon","doi":"10.1214/19-aoas1261","DOIUrl":"10.1214/19-aoas1261","url":null,"abstract":"We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive partitioning step. Benchmark results using simulated and real data indicate that the ORSF's predicted risk function has high prognostic value in comparison to random survival forests, conditional inference forests, regression, and boosting. In an application to data from the Jackson Heart Study, we demonstrate variable and partial dependence using the ORSF and highlight characteristics of its 10-year predicted risk function for atherosclerotic cardiovascular disease events (ASCVD; stroke, coronary heart disease). We present visualizations comparing variable and partial effect estimation according to the ORSF, the conditional inference forest, and the Pooled Cohort Risk equations. The obliqueRSF R package, which provides functions to fit the ORSF and create variable and partial dependence plots, is available on the comprehensive R archive network (CRAN).","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 3","pages":"1847-1883"},"PeriodicalIF":1.3,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875945/pdf/nihms-1827649.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9526817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS. 多重中介的贝叶斯方法：发电厂排放控制分析中的关联主分层和因果中介。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-09-01 Epub Date: 2019-10-17 DOI: 10.1214/19-AOAS1260

Chanmin Kim, Michael J Daniels, Joseph W Hogan, Christine Choirat, Corwin M Zigler

{"title":"BAYESIAN METHODS FOR MULTIPLE MEDIATORS: RELATING PRINCIPAL STRATIFICATION AND CAUSAL MEDIATION IN THE ANALYSIS OF POWER PLANT EMISSION CONTROLS.","authors":"Chanmin Kim, Michael J Daniels, Joseph W Hogan, Christine Choirat, Corwin M Zigler","doi":"10.1214/19-AOAS1260","DOIUrl":"https://doi.org/10.1214/19-AOAS1260","url":null,"abstract":"Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution, and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect of a particular control technology on ambient pollution is mediated through causal effects on power plant emissions. Since power plants emit various compounds that contribute to ambient pollution, we develop new methods for multiple intermediate variables that are measured contemporaneously, may interact with one another, and may exhibit joint mediating effects. Specifically, we propose new methods leveraging two related frameworks for causal inference in the presence of mediating variables: principal stratification and causal mediation analysis. We define principal effects based on multiple mediators, and also introduce a new decomposition of the total effect of an intervention on ambient pollution into the natural direct effect and natural indirect effects for all combinations of mediators. Both approaches are anchored to the same observed-data models, which we specify with Bayesian nonparametric techniques. We provide assumptions for estimating principal causal effects, then augment these with an additional assumption required for causal mediation analysis. The two analyses, interpreted in tandem, provide the first empirical investigation of the presumed causal pathways that motivate important air quality regulatory policies.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 3","pages":"1927-1956"},"PeriodicalIF":1.8,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814408/pdf/nihms-1053558.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

GRAPHICAL MODELS FOR ZERO-INFLATED SINGLE CELL GENE EXPRESSION. 零平面单细胞基因表达的图形模型。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-06-01 Epub Date: 2019-06-17 DOI: 10.1214/18-AOAS1213

Andrew McDavid, Raphael Gottardo, Noah Simon, Mathias Drton

{"title":"GRAPHICAL MODELS FOR ZERO-INFLATED SINGLE CELL GENE EXPRESSION.","authors":"Andrew McDavid, Raphael Gottardo, Noah Simon, Mathias Drton","doi":"10.1214/18-AOAS1213","DOIUrl":"https://doi.org/10.1214/18-AOAS1213","url":null,"abstract":"Bulk gene expression experiments relied on aggregations of thousands of cells to measure the average expression in an organism. Advances in microfluidic and droplet sequencing now permit expression profiling in single cells. This study of cell-to-cell variation reveals that individual cells lack detectable expression of transcripts that appear abundant on a population level, giving rise to zero-inflated expression patterns. To infer gene co-regulatory networks from such data, we propose a multivariate Hurdle model. It is comprised of a mixture of singular Gaussian distributions. We employ neighborhood selection with the pseudo-likelihood and a group lasso penalty to select and fit undirected graphical models that capture conditional independences between genes. The proposed method is more sensitive than existing approaches in simulations, even under departures from our Hurdle model. The method is applied to data for T follicular helper cells, and a high-dimensional profile of mouse dendritic cells. It infers network structure not revealed by other methods; or in bulk data sets. An R implementation is available at https://github.com/amcdavid/HurdleNormal.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 2","pages":"848-873"},"PeriodicalIF":1.8,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1214/18-AOAS1213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

FUSED COMPARATIVE INTERVENTION SCORING FOR HETEROGENEITY OF LONGITUDINAL INTERVENTION EFFECTS. 纵向干预效果异质性的融合比较干预评分。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-06-01 DOI: 10.1214/18-aoas1216

Jared D Huling, Menggang Yu, Maureen Smith

{"title":"FUSED COMPARATIVE INTERVENTION SCORING FOR HETEROGENEITY OF LONGITUDINAL INTERVENTION EFFECTS.","authors":"Jared D Huling, Menggang Yu, Maureen Smith","doi":"10.1214/18-aoas1216","DOIUrl":"https://doi.org/10.1214/18-aoas1216","url":null,"abstract":"With the growing cost of health care in the United States, the need to improve efficiency and efficacy has become increasingly urgent. There has been a keen interest in developing interventions to effectively coordinate the typically fragmented care of patients with many comorbidities. Evaluation of such interventions is often challenging given their long-term nature and their differential effectiveness among different patients. Furthermore, care coordination interventions are often highly resource-intensive. Hence there is pressing need to identify which patients would benefit the most from a care coordination program. In this work we introduce a subgroup identification procedure for long-term interventions whose effects are expected to change smoothly over time. We allow differential effects of an intervention to vary over time and encourage these effects to be more similar for closer time points by utilizing a fused lasso penalty. Our approach allows for flexible modeling of temporally changing intervention effects while also borrowing strength in estimation over time. We utilize our approach to construct a personalized enrollment decision rule for a complex case management intervention in a large health system and demonstrate that the enrollment decision rule results in improvement in health outcomes and care costs. The proposed methodology could have broad usage for the analysis of different types of long-term interventions or treatments including other interventions commonly implemented in health systems.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 2","pages":"824-847"},"PeriodicalIF":1.8,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1214/18-aoas1216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9455781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

BAYESIAN LATENT HIERARCHICAL MODEL FOR TRANSCRIPTOMIC META-ANALYSIS TO DETECT BIOMARKERS WITH CLUSTERED META-PATTERNS OF DIFFERENTIAL EXPRESSION SIGNALS. 用于转录组元分析的贝叶斯潜在层次模型，用于检测具有差异表达信号的聚类元模式的生物标志物。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2019-03-01 Epub Date: 2019-04-10 DOI: 10.1214/18-AOAS1188

Zhiguang Huo, Chi Song, George Tseng

{"title":"BAYESIAN LATENT HIERARCHICAL MODEL FOR TRANSCRIPTOMIC META-ANALYSIS TO DETECT BIOMARKERS WITH CLUSTERED META-PATTERNS OF DIFFERENTIAL EXPRESSION SIGNALS.","authors":"Zhiguang Huo, Chi Song, George Tseng","doi":"10.1214/18-AOAS1188","DOIUrl":"10.1214/18-AOAS1188","url":null,"abstract":"Due to the rapid development of high-throughput experimental techniques and fast-dropping prices, many transcriptomic datasets have been generated and accumulated in the public domain. Meta-analysis combining multiple transcriptomic studies can increase the statistical power to detect disease-related biomarkers. In this paper, we introduce a Bayesian latent hierarchical model to perform transcriptomic meta-analysis. This method is capable of detecting genes that are differentially expressed (DE) in only a subset of the combined studies, and the latent variables help quantify homogeneous and heterogeneous differential expression signals across studies. A tight clustering algorithm is applied to detected biomarkers to capture differential meta-patterns that are informative to guide further biological investigation. Simulations and three examples, including a microarray dataset from metabolism-related knockout mice, an RNA-seq dataset from HIV transgenic rats, and cross-platform datasets from human breast cancer, are used to demonstrate the performance of the proposed method.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 1","pages":"340-366"},"PeriodicalIF":1.3,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472949/pdf/nihms-977410.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37171811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients. 食管癌症患者复发性不良事件和生存率的贝叶斯半参数联合回归分析。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2019-03-01 Epub Date: 2019-04-10 DOI: 10.1214/18-AOAS1182

Juhee Lee, Peter F Thall, Steven H Lin

{"title":"Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients.","authors":"Juhee Lee, Peter F Thall, Steven H Lin","doi":"10.1214/18-AOAS1182","DOIUrl":"10.1214/18-AOAS1182","url":null,"abstract":"We propose a Bayesian semiparametric joint regression model for a recurrent event process and survival time. Assuming independent latent subject frailties, we define marginal models for the recurrent event process intensity and survival distribution as functions of the subject's frailty and baseline covariates. A robust Bayesian model, called Joint-DP, is obtained by assuming a Dirichlet process for the frailty distribution. We present a simulation study that compares posterior estimates under the Joint-DP model to a Bayesian joint model with lognormal frailties, a frequentist joint model, and marginal models for either the recurrent event process or survival time. The simulations show that the Joint-DP model does a good job of correcting for treatment assignment bias, and has favorable estimation reliability and accuracy compared with the alternative models. The Joint-DP model is applied to analyze an observational dataset from esophageal cancer patients treated with chemo-radiation, including the times of recurrent effusions of fluid to the heart or lungs, survival time, prognostic covariates, and radiation therapy modality.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 1","pages":"221-247"},"PeriodicalIF":1.3,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824476/pdf/nihms969597.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JOINT MEAN AND COVARIANCE MODELING OF MULTIPLE HEALTH OUTCOME MEASURES. 多种健康结果测量的联合均值和协方差建模。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-03-01 Epub Date: 2019-04-10 DOI: 10.1214/18-AOAS1187

Xiaoyue Niu, Peter D Hoff

引用次数: 3

CAUSAL INFERENCE IN THE CONTEXT OF AN ERROR PRONE EXPOSURE: AIR POLLUTION AND MORTALITY. 容易出错的暴露背景下的因果推断：空气污染和死亡率。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-03-01 Epub Date: 2019-04-10 DOI: 10.1214/18-AOAS1206

Xiao Wu, Danielle Braun, Marianthi-Anna Kioumourtzoglou, Christine Choirat, Qian Di, Francesca Dominici

{"title":"CAUSAL INFERENCE IN THE CONTEXT OF AN ERROR PRONE EXPOSURE: AIR POLLUTION AND MORTALITY.","authors":"Xiao Wu, Danielle Braun, Marianthi-Anna Kioumourtzoglou, Christine Choirat, Qian Di, Francesca Dominici","doi":"10.1214/18-AOAS1206","DOIUrl":"https://doi.org/10.1214/18-AOAS1206","url":null,"abstract":"We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS). Using validation data, we propose a regression calibration (RC)-based adjustment for a continuous error-prone exposure combined with GPS to adjust for confounding (RC-GPS). The outcome analysis is conducted after transforming the corrected continuous exposure into a categorical exposure. We consider confounding adjustment in the context of GPS subclassification, inverse probability treatment weighting (IPTW) and matching. In simulations with varying degrees of exposure error and confounding bias, RC-GPS eliminates bias from exposure error and confounding compared to standard approaches that rely on the error-prone exposure. We applied RC-GPS to a rich data platform to estimate the causal effect of long-term exposure to fine particles (PM2.5) on mortality in New England for the period from 2000 to 2012. The main study consists of 2202 zip codes covered by 217,660 1 km × 1 km grid cells with yearly mortality rates, yearly PM2.5 averages estimated from a spatio-temporal model (error-prone exposure) and several potential confounders. The internal validation study includes a subset of 83 1 km × 1 km grid cells within 75 zip codes from the main study with error-free yearly PM2.5 exposures obtained from monitor stations. Under assumptions of noninterference and weak unconfoundedness, using matching we found that exposure to moderate levels of PM2.5 (8 < PM2.5 ≤ 10 μg/m3) causes a 2.8% (95% CI: 0.6%, 3.6%) increase in all-cause mortality compared to low exposure (PM2.5 ≤ 8 μg/m3).","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 1","pages":"520-547"},"PeriodicalIF":1.8,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1214/18-AOAS1206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS. 多层敲除滤波器：在多分辨率下控制变量选择。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2019-03-01 Epub Date: 2019-04-10 DOI: 10.1214/18-AOAS1185

Eugene Katsevich, Chiara Sabatti

{"title":"MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS.","authors":"Eugene Katsevich, Chiara Sabatti","doi":"10.1214/18-AOAS1185","DOIUrl":"https://doi.org/10.1214/18-AOAS1185","url":null,"abstract":"We tackle the problem of selecting from among a large number of variables those that are \"important\" for an outcome. We consider situations where groups of variables are also of interest. For example, each variable might be a genetic polymorphism, and we might want to study how a trait depends on variability in genes, segments of DNA that typically contain multiple such polymorphisms. In this context, to discover that a variable is relevant for the outcome implies discovering that the larger entity it represents is also important. To guarantee meaningful results with high chance of replicability, we suggest controlling the rate of false discoveries for findings at the level of individual variables and at the level of groups. Building on the knockoff construction of Barber and Candès [Ann. Statist. 43 (2015) 2055-2085] and the multilayer testing framework of Barber and Ramdas [J. Roy. Statist. Soc. Ser. B 79 (2017) 1247-1268], we introduce the multilayer knockoff filter (MKF). We prove that MKF simultaneously controls the FDR at each resolution and use simulations to show that it incurs little power loss compared to methods that provide guarantees only for the discoveries of individual variables. We apply MKF to analyze a genetic dataset and find that it successfully reduces the number of false gene discoveries without a significant reduction in power.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"13 1","pages":"1-33"},"PeriodicalIF":1.8,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1214/18-AOAS1185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41219385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45