Journal of Applied Statistics最新文献_第4页

Semiparametric model averaging prediction in nested case-control studies. 嵌套病例对照研究中的半参数模型平均预测。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-12-31 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2447324

Mengyu Li, Xiaoguang Wang

{"title":"Semiparametric model averaging prediction in nested case-control studies.","authors":"Mengyu Li, Xiaoguang Wang","doi":"10.1080/02664763.2024.2447324","DOIUrl":"https://doi.org/10.1080/02664763.2024.2447324","url":null,"abstract":"Survival predictions for patients are becoming increasingly important in clinical practice as they play a crucial role in aiding healthcare professionals to make more informed diagnoses and treatment decisions. The nested case-control designs have been extensively utilized as a cost-effective solution in many large cohort studies across epidemiology and other research fields. To achieve accurate survival predictions of individuals from nested case-control studies, we propose a semiparametric model averaging approach based on the partly linear additive proportional hazards structure to avoid the curse of dimensionality. The inverse probability weighting method is considered to estimate the parameters of submodels used in model averaging. We choose the weights by maximizing the pseudo-likelihood function constructed for the aggregated model and discuss the asymptotic optimality of selected weights. Simulation studies are conducted to assess the performance of our proposed model averaging method in the nested case-control study. Furthermore, we apply the proposed approach to real data to demonstrate its superiority.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 10","pages":"1904-1930"},"PeriodicalIF":1.1,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144789264","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

Adapting and evaluating deep-pseudo neural network for survival data with time-varying covariates. 具有时变协变量的生存数据的深度伪神经网络自适应与评价。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-12-24 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2444649

Albert Whata, Justine B Nasejje, Najmeh Nakhaei Rad, Tshilidzi Mulaudzi, Ding-Geng Chen

{"title":"Adapting and evaluating deep-pseudo neural network for survival data with time-varying covariates.","authors":"Albert Whata, Justine B Nasejje, Najmeh Nakhaei Rad, Tshilidzi Mulaudzi, Ding-Geng Chen","doi":"10.1080/02664763.2024.2444649","DOIUrl":"https://doi.org/10.1080/02664763.2024.2444649","url":null,"abstract":"The Extended Cox model provides an alternative to the proportional hazard Cox model for modelling data including time-varying covariates. Incorporating time-varying covariates is particularly beneficial when dealing with survival data, as it can improve the precision of survival function estimation. Deep learning methods, in particular, the Deep-pseudo survival neural network (DSNN) model have demonstrated a high potential for accurately predicting right-censored survival data when dealing with time-invariant variables. The DSNN's ability to discretise survival times makes it a natural choice for extending its application to scenarios involving time-varying covariates. This study adapts the DSNN to predict survival probabilities for data with time-varying covariates. To demonstrate this, we considered two scenarios: significant and non-significant time-varying covariates. For significant covariates, the Brier scores were below 0.25 at all considered specific time points, while, in the non-significant case, the Brier scores were above 0.25. The results illustrate that the DSNN performed comparably to the extended Cox, the Dynamic-DeepHit and mulitivariate joint models and on the simulated data. A real-world data application further confirms the predictive potential of the DSNN model in modelling survival data with time-varying covariates.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 10","pages":"1847-1870"},"PeriodicalIF":1.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144789261","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

Latent class profile model with time-dependent covariates: a study on symptom patterning of patients for head and neck cancer. 具有时间相关协变量的潜在类特征模型：头颈癌患者症状模式的研究。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-16 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2435997

Jung Wun Lee, Hayley Dunnack Yackel

引用次数: 0

A control chart for bivariate discrete data monitoring. 用于二元离散数据监测的控制图。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-15 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2438795

Ayesha Talib, Sajid Ali, Ismail Shah

引用次数: 0

Rollout designs for lump-sum data. 一次性数据的推出设计。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-13 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2440031

Qunzhi Xu, Hongzhen Tian, Ananda Sarkar, Yajun Mei

引用次数: 0

Delaying bud-break on pecan trees: a Bayesian longitudinal multinomial regression approach. 核桃树延迟发芽：贝叶斯纵向多项回归方法。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-12 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2436007

Dayna P Saldaña Zepeda, Richard Heerema, Ciro Velasco Cruz, William Giese, Joshua Sherman

{"title":"Delaying bud-break on pecan trees: a Bayesian longitudinal multinomial regression approach.","authors":"Dayna P Saldaña Zepeda, Richard Heerema, Ciro Velasco Cruz, William Giese, Joshua Sherman","doi":"10.1080/02664763.2024.2436007","DOIUrl":"10.1080/02664763.2024.2436007","url":null,"abstract":"A multivariate Bayesian Probit model is adapted to analyze a longitudinal multiclass-ordinal response, with a linear plateau as the longitudinal model. Measurements on pecan bud growth were collected on irregular time intervals, about a week apart from late March to mid April, using a six-level ordinal scale. The data are from two randomized complete block designs with four blocks each. The experiments were setup and initiated in 2018 in a pecan orchard, at two different locations, to evaluate the effect of two sets of four treatments on delaying growth of recently broken pecan buds to minimize bud loss due to low temperatures. A simulation study was successfully carried out to validate the model implementation. Treatment 3 of Experiment 1 was associated with the greatest reduction in bud growth rate. In Experiment 2, Treatments 2 and 3 had some effect on delaying bud growth. Although treatment effects were not statistically different in either experiment, this paper presents a practical and efficient modeling technique for longitudinal multinomial ordinal data, a common data type in applied agricultural research studies.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 8","pages":"1649-1669"},"PeriodicalIF":1.2,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12147487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144266339","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

Interval-valued scalar-on-function linear quantile regression based on the bivariate center and radius method. 基于二元中心和半径法的区间值函数上标度线性分位数回归。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2440035

Kaiyuan Liu, Min Xu, Jiang Du, Tianfa Xie

引用次数: 0

Scalable Bayesian inference for bradley-Terry models with ties: an application to honour based abuse. 带联系的bradley-Terry模型的可扩展贝叶斯推理：基于荣誉滥用的应用。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2436608

Rowland G Seymour, Fabian Hernandez

{"title":"Scalable Bayesian inference for bradley-Terry models with ties: an application to honour based abuse.","authors":"Rowland G Seymour, Fabian Hernandez","doi":"10.1080/02664763.2024.2436608","DOIUrl":"https://doi.org/10.1080/02664763.2024.2436608","url":null,"abstract":"Honour-based abuse covers a wide range of family abuse including female genital mutilation and forced marriage. Safeguarding professionals need to identify where abuses are happening in their local community to the best support those at risk of these crimes and take preventative action. However, there is little local data about these kinds of crime. To tackle this problem, we ran comparative judgement surveys to map abuses at the local level, where participants where shown pairs of wards and asked which had a higher rate of honour based abuse. In previous comparative judgement studies, participants reported fatigue associated with comparisons between areas with similar levels of abuse. Allowing for tied comparisons reduces fatigue, but increase the computational complexity when fitting the model. We designed an efficient Markov Chain Monte Carlo algorithm to fit a model with ties, allowing for a wide range of prior distributions on the model parameters. Working with South Yorkshire Police and Oxford Against Cutting, we mapped the risk of honour-based abuse at the community level in two counties in the UK.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 9","pages":"1695-1712"},"PeriodicalIF":1.2,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12217112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560152","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

Estimation for time-varying coefficient smoothed quantile regression. 时变系数的平滑分位数回归估计。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-10 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2440056

Lixia Hu, Jinhong You, Qian Huang, Shu Liu

引用次数: 0

Integrative analysis of high-dimensional quantile regression with contrasted penalization. 高维分位数回归与对比惩罚的综合分析。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-12-10 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2438799

Panpan Ren, Xu Liu, Xiao Zhang, Peng Zhan, Tingting Qiu

{"title":"Integrative analysis of high-dimensional quantile regression with contrasted penalization.","authors":"Panpan Ren, Xu Liu, Xiao Zhang, Peng Zhan, Tingting Qiu","doi":"10.1080/02664763.2024.2438799","DOIUrl":"10.1080/02664763.2024.2438799","url":null,"abstract":"In the era of big data, the simultaneous analysis of multiple high-dimensional, heavy-tailed datasets has become essential. Integrative analysis offers a powerful approach to combine and synthesize information from these various datasets, and often outperforming traditional meta-analysis and single-dataset analysis. In this paper, we introduce a novel high-dimensional integrative quantile regression that can accommodate the complexities inherent in multi-dataset analysis. A contrast penalty that smooths regression coefficients is introduced to account for across-dataset structures and improve variable selection. To ease the computational burden associated with high-dimensional quantile regression, a new algorithm is developed that is effective at computing solution paths and selecting significant variables. Monte Carlo simulations demonstrate its competitive performance. Additionally, the proposed method is applied to data from the China Health and Retirement Longitudinal Study, illustrating its practical utility in identifying influential factors affecting support income for the elderly. Findings indicate that adult children's individual characteristics and emotional comfort are primary factors of support income, and the extent of their impact varies across regions.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 9","pages":"1760-1776"},"PeriodicalIF":1.2,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12217111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560149","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