JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS(英文缩写 J R STAT SOC C-APPL),ISSN 0035-9254,eISSN 1467-9876 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。
指标来源：jcr_cas_ifqb
期刊简介
暂无简介。
历年影响因子趋势
| 年份 | 影响因子 | JCR 分区 |
|---|---|---|
| - | Q2 | |
| - | Q2 | |
| - | Q3 | |
| - | Q2 | |
| - | Q2 |
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 最新收录文献
※ 中文译文由 AI 辅助生成,仅供学术参考,请以英文原文为准。
-
Refining genetic discoveries of group knockoffs via a feature-level filter.
Identifying variants that carry substantial information on the trait of interest remains a core topic in genetic studies. In analysing the EADB-UKBB dataset to identify genetic variants associated wit…
-
Refined trial sequential analysis for meta-analyses with few studies.
Evidence syntheses are often updated as new trials become available. A cumulative meta-analysis repeats a meta-analysis in chronological order, and trial sequential analysis applies group-sequential p…
-
Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure.
Precision environmental health seeks to estimate how the effects of the environment vary across the population to inform targeted interventions and public health policy. However, there is a lack of st…
-
Modeling Cure from Cancer Accounting for Inevitable Mortality. 对癌症的治愈进行建模，说明不可避免的死亡率
Cancer remains the second most prevalent cause of death in the United States, claiming 605,213 lives in 2021, surpassing COVID-19 deaths. The cancer mortality rate continued to decline between 2019 an…
-
Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.
Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding the…
-
{"_":"Adaptive Fisher's method using weakly geometric grid for combining -values with application to COVID-19 surveillance.","i":["p"]}
In COVID-19 surveillance, detecting significant case increases within regions over specific periods is crucial. Classical methods, typically relying on strict parametric assumptions, struggle with the…
-
Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach.
Place-based epidemiology studies often rely on circular buffers to define 'exposure' to spatially distributed risk factors, where the buffer radius represents a threshold beyond which exposure does no…
-
Robust domain selection for functional data via interval-wise testing and effect size mapping.
Among inferential problems in functional data analysis, domain selection is one of the practical interests aiming to identify sub-interval(s) of the domain where desired functional features are displa…
-
Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.
When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To advance individualized t…
-
Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.
A discrete spatial lattice can be cast as a network structure over which spatially-correlated outcomes are observed. A second network structure may also capture similarities among measured features, w…