Computational Statistics最新文献_第4页

Multiple imputation with competing risk outcomes 具有竞争风险结果的多重估算

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-26 DOI: 10.1007/s00180-024-01518-w

Peter C. Austin

{"title":"Multiple imputation with competing risk outcomes","authors":"Peter C. Austin","doi":"10.1007/s00180-024-01518-w","DOIUrl":"https://doi.org/10.1007/s00180-024-01518-w","url":null,"abstract":"In time-to-event analyses, a competing risk is an event whose occurrence precludes the occurrence of the event of interest. Settings with competing risks occur frequently in clinical research. Missing data, which is a common problem in research, occurs when the value of a variable is recorded for some, but not all, records in the dataset. Multiple Imputation (MI) is a popular method to address the presence of missing data. MI uses an imputation model to generate M (M > 1) values for each variable that is missing, resulting in the creation of M complete datasets. A popular algorithm for imputing missing data is multivariate imputation using chained equations (MICE). We used a complex simulation design with covariates and missing data patterns reflective of patients hospitalized with acute myocardial infarction (AMI) to compare three strategies for imputing missing predictor variables when the analysis model is a cause-specific hazard when there were three different event types. We compared two MICE-based strategies that differed according to which cause-specific cumulative hazard functions were included in the imputation models (the three cause-specific cumulative hazard functions vs. only the cause-specific cumulative hazard function for the primary outcome) with the use of the substantive model compatible fully conditional specification (SMCFCS) algorithm. While no strategy had consistently superior performance compared to the other strategies, SMCFCS may be the preferred strategy. We illustrated the application of the strategies using a case study of patients hospitalized with AMI.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"176 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking handball teams from statistical strength estimation 根据统计实力估算手球队排名

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-24 DOI: 10.1007/s00180-024-01522-0

Florian Felice

引用次数: 0

Hypothesis testing in Cox models when continuous covariates are dichotomized: bias analysis and bootstrap-based test 连续协变量二分时 Cox 模型中的假设检验：偏差分析和基于引导的检验

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-23 DOI: 10.1007/s00180-024-01520-2

Hyunman Sim, Sungjeong Lee, Bo-Hyung Kim, Eun Shin, Woojoo Lee

引用次数: 0

Trend of high dimensional time series estimation using low-rank matrix factorization: heuristics and numerical experiments via the TrendTM package 使用低秩矩阵因式分解进行高维时间序列估计的趋势：通过 TrendTM 软件包进行启发式方法和数值实验

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-20 DOI: 10.1007/s00180-024-01519-9

Emilie Lebarbier, Nicolas Marie, Amélie Rosier

引用次数: 0

Some aspects of nonlinear dimensionality reduction 非线性降维的一些方面

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-16 DOI: 10.1007/s00180-024-01514-0

Liwen Wang, Yongda Wang, Shifeng Xiong, Jiankui Yang

引用次数: 0

Double truncation method for controlling local false discovery rate in case of spiky null 控制尖空情况下局部误发现率的双重截断法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-05 DOI: 10.1007/s00180-024-01510-4

Shinjune Kim, Youngjae Oh, Johan Lim, DoHwan Park, Erin M. Green, Mark L. Ramos, Jaesik Jeong

{"title":"Double truncation method for controlling local false discovery rate in case of spiky null","authors":"Shinjune Kim, Youngjae Oh, Johan Lim, DoHwan Park, Erin M. Green, Mark L. Ramos, Jaesik Jeong","doi":"10.1007/s00180-024-01510-4","DOIUrl":"https://doi.org/10.1007/s00180-024-01510-4","url":null,"abstract":"Many multiple test procedures, which control the false discovery rate, have been developed to identify some cases (e.g. genes) showing statistically significant difference between two different groups. However, a common issue encountered in some practical data sets is the presence of highly spiky null distributions. Existing methods struggle to control type I error in such cases due to the “inflated false positives,\" but this problem has not been addressed in previous literature. Our team recently encountered this issue while analyzing SET4 gene deletion data and proposed modeling the null distribution using a scale mixture normal distribution. However, the use of this approach is limited due to strong assumptions on the spiky peak. In this paper, we present a novel multiple test procedure that can be applied to any type of spiky peak data, including situations with no spiky peak or with one or two spiky peaks. Our approach involves truncating the central statistics around 0, which primarily contribute to the null spike, as well as the two tails that may be contaminated by alternative distributions. We refer to this method as the “double truncation method.\" After applying double truncation, we estimate the null density using the doubly truncated maximum likelihood estimator. We demonstrate numerically that our proposed method effectively controls the false discovery rate at the desired level using simulated data. Furthermore, we apply our method to two real data sets, namely the SET protein data and peony data.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"25 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Asymptotic properties of kernel density and hazard rate function estimators with censored widely orthant dependent data 核密度和危险率函数估计器的渐近特性与普查广泛正交依存数据

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-06-03 DOI: 10.1007/s00180-024-01509-x

Yi Wu, Wei Wang, Wei Yu, Xuejun Wang

引用次数: 0

Expectile regression averaging method for probabilistic forecasting of electricity prices 用于电价概率预测的期望回归平均法

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-05-29 DOI: 10.1007/s00180-024-01508-y

Joanna Janczura

引用次数: 0

Projection predictive variable selection for discrete response families with finite support 有限支持离散响应族的投影预测变量选择

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-05-29 DOI: 10.1007/s00180-024-01506-0

Frank Weber, Änne Glass, Aki Vehtari

{"title":"Projection predictive variable selection for discrete response families with finite support","authors":"Frank Weber, Änne Glass, Aki Vehtari","doi":"10.1007/s00180-024-01506-0","DOIUrl":"https://doi.org/10.1007/s00180-024-01506-0","url":null,"abstract":"The projection predictive variable selection is a decision-theoretically justified Bayesian variable selection approach achieving an outstanding trade-off between predictive performance and sparsity. Its projection problem is not easy to solve in general because it is based on the Kullback–Leibler divergence from a restricted posterior predictive distribution of the so-called reference model to the parameter-conditional predictive distribution of a candidate model. Previous work showed how this projection problem can be solved for response families employed in generalized linear models and how an approximate latent-space approach can be used for many other response families. Here, we present an exact projection method for all response families with discrete and finite support, called the augmented-data projection. A simulation study for an ordinal response family shows that the proposed method performs better than or similarly to the previously proposed approximate latent-space projection. The cost of the slightly better performance of the augmented-data projection is a substantial increase in runtime. Thus, if the augmented-data projection’s runtime is too high, we recommend the latent projection in the early phase of the model-building workflow and the augmented-data projection for final results. The ordinal response family from our simulation study is supported by both projection methods, but we also include a real-world cancer subtyping example with a nominal response family, a case that is not supported by the latent projection.","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"42 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient regression analyses with zero-augmented models based on ranking 基于排序的零增强模型的高效回归分析

IF 1.3 4区数学

Computational Statistics Pub Date : 2024-05-14 DOI: 10.1007/s00180-024-01503-3

Deborah Kanda, Jingjing Yin, Xinyan Zhang, Hani Samawi

引用次数: 0