Journal of Applied Statistics最新文献_第2页

An optimal subsampling design for large-scale Cox model with censored data. 带截尾数据的大尺度Cox模型的最优次抽样设计。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-04 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2423234

Shiqi Liu, Zilong Xie, Ming Zheng, Wen Yu

{"title":"An optimal subsampling design for large-scale Cox model with censored data.","authors":"Shiqi Liu, Zilong Xie, Ming Zheng, Wen Yu","doi":"10.1080/02664763.2024.2423234","DOIUrl":"10.1080/02664763.2024.2423234","url":null,"abstract":"Subsampling designs are useful for reducing computational load and storage cost for large-scale data analysis. For massive survival data with right censoring, we propose a class of optimal subsampling designs under the widely-used Cox model. The proposed designs utilize information from both the outcome and the covariates. Different forms of the design can be derived adaptively to meet various targets, such as optimizing the overall estimation accuracy or minimizing the variation of specific linear combination of the estimators. Given the subsampled data, the inverse probability weighting approach is employed to estimate the model parameters. The resultant estimators are shown to be consistent and asymptotically normally distributed. Simulation results indicate that the proposed subsampling design yields more efficient estimators than the uniform subsampling by using subsampled data of comparable sample sizes. Additionally, the subsampling estimation significantly reduces the computational load and storage cost relative to the full data estimation. An analysis of a real data example is provided for illustration.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 7","pages":"1315-1341"},"PeriodicalIF":1.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199240","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

Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data. 利用复杂值fMRI数据进行脑活动映射的高效全贝叶斯方法。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-04 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2422392

Zhengxin Wang, Daniel B Rowe, Xinyi Li, D Andrew Brown

引用次数: 0

Prediction intervals and bands with improved coverage for functional data under noisy discrete observation. 在有噪声的离散观测条件下，提高功能数据覆盖范围的预测区间和频带。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-28 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2420223

David Kraus

{"title":"Prediction intervals and bands with improved coverage for functional data under noisy discrete observation.","authors":"David Kraus","doi":"10.1080/02664763.2024.2420223","DOIUrl":"https://doi.org/10.1080/02664763.2024.2420223","url":null,"abstract":"We revisit the classic situation in functional data analysis in which curves are observed at discrete, possibly sparse and irregular, arguments with observation noise. We focus on the reconstruction of individual curves by prediction intervals and bands. The standard approach consists of two steps: first, one estimates the mean and covariance function of curves and observation noise variance function by, e.g. penalized splines, and second, under Gaussian assumptions, one derives the conditional distribution of a curve given observed data and constructs prediction sets with required properties, usually employing sampling from the predictive distribution. This approach is well established, commonly used and theoretically valid but practically, it surprisingly fails in its key property: prediction sets constructed this way often do not have the required coverage. The actual coverage is lower than the nominal one. We investigate the cause of this issue and propose a computationally feasible remedy that leads to prediction regions with a much better coverage. Our method accounts for the uncertainty of the predictive model by sampling from the approximate distribution of its spline estimators whose covariance is estimated by a novel sandwich estimator. Our approach also applies to the important case of covariate-adjusted models.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1258-1277"},"PeriodicalIF":1.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010105","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

A non-linear integer-valued autoregressive model with zero-inflated data series. 具有零膨胀数据序列的非线性整数值自回归模型。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-26 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2419495

Predrag M Popović, Hassan S Bakouch, Miroslav M Ristić

引用次数: 0

Evaluating the median p-value method for assessing the statistical significance of tests when using multiple imputation. 评估中位p值方法，用于评估多重输入时检验的统计显著性。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-25 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2418473

Peter C Austin, Iris Eekhout, Stef van Buuren

{"title":"Evaluating the median p-value method for assessing the statistical significance of tests when using multiple imputation.","authors":"Peter C Austin, Iris Eekhout, Stef van Buuren","doi":"10.1080/02664763.2024.2418473","DOIUrl":"https://doi.org/10.1080/02664763.2024.2418473","url":null,"abstract":"Rubin's Rules are commonly used to pool the results of statistical analyses across imputed samples when using multiple imputation. Rubin's Rules cannot be used when the result of an analysis in an imputed dataset is not a statistic and its associated standard error, but a test statistic (e.g. Student's t-test). While complex methods have been proposed for pooling test statistics across imputed samples, these methods have not been implemented in many popular statistical software packages. The median p-value method has been proposed for pooling test statistics. The statistical significance level of the pooled test statistic is the median of the associated p-values across the imputed samples. We evaluated the performance of this method with nine statistical tests: Student's t-test, Wilcoxon Rank Sum test, Analysis of Variance, Kruskal-Wallis test, the test of significance for Pearson's and Spearman's correlation coefficient, the Chi-squared test, the test of significance for a regression coefficient from a linear regression and from a logistic regression. For each test, the empirical type I error rate was higher than the advertised rate. The magnitude of inflation increased as the prevalence of missing data increased. The median p-value method should not be used to assess statistical significance across imputed datasets.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1161-1176"},"PeriodicalIF":1.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012737","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

Mitigating the choice of the duration in DDMS models through a parametric link. 通过参数链接减少了DDMS模型中持续时间的选择。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-24 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2419505

Fernando Henrique de Paula E Silva Mendes, Douglas Eduardo Turatti, Guilherme Pumi

{"title":"Mitigating the choice of the duration in DDMS models through a parametric link.","authors":"Fernando Henrique de Paula E Silva Mendes, Douglas Eduardo Turatti, Guilherme Pumi","doi":"10.1080/02664763.2024.2419505","DOIUrl":"https://doi.org/10.1080/02664763.2024.2419505","url":null,"abstract":"One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The novelty of this paper is the use of the asymmetric Aranda-Ordaz parametric link function to model transition probabilities in DDMS models, instead of the commonly applied logit link. The idea behind this approach is that any incorrect duration choice is compensated for by the parameter in the link, increasing model flexibility. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S&P500, which showcases the capabilities of the proposed model.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1219-1238"},"PeriodicalIF":1.2,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144018792","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

A semiparametric accelerated failure time-based mixture cure tree. 基于半参数加速失效时间的混合修复树。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-23 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2418476

Wisdom Aselisewine, Suvra Pal, Helton Saulo

{"title":"A semiparametric accelerated failure time-based mixture cure tree.","authors":"Wisdom Aselisewine, Suvra Pal, Helton Saulo","doi":"10.1080/02664763.2024.2418476","DOIUrl":"https://doi.org/10.1080/02664763.2024.2418476","url":null,"abstract":"The mixture cure rate model (MCM) is the most widely used model for the analysis of survival data with a cured subgroup. In this context, the most common strategy to model the cure probability is to assume a generalized linear model with a known link function, such as the logit link function. However, the logit model can only capture simple effects of covariates on the cure probability. In this article, we propose a new MCM where the cure probability is modeled using a decision tree-based classifier and the survival distribution of the uncured is modeled using an accelerated failure time structure. To estimate the model parameters, we develop an expectation maximization algorithm. Our simulation study shows that the proposed model performs better in capturing nonlinear classification boundaries when compared to the logit-based MCM and the spline-based MCM. This results in more accurate and precise estimates of the cured probabilities, which in-turn results in improved predictive accuracy of cure. We further show that capturing nonlinear classification boundary also improves the estimation results corresponding to the survival distribution of the uncured subjects. Finally, we apply our proposed model and the EM algorithm to analyze an existing bone marrow transplant data.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1177-1194"},"PeriodicalIF":1.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020246","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

The PCovR biplot: a graphical tool for principal covariates regression. PCovR双标图：主协变量回归的图形工具。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-18 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2417978

Elisa Frutos-Bernal, José Luis Vicente-Villardón

引用次数: 0

Reliability analysis based on doubly-truncated and interval-censored data. 基于双截尾和间隔截尾数据的可靠性分析。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-14 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2415412

Pao-Sheng Shen, Huai-Man Li

引用次数: 0

A novel ranked k-nearest neighbors algorithm for missing data imputation. 一种新的k近邻排序缺失数据输入算法。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2414357

Yasir Khan, Said Farooq Shah, Syed Muhammad Asim

{"title":"A novel ranked k-nearest neighbors algorithm for missing data imputation.","authors":"Yasir Khan, Said Farooq Shah, Syed Muhammad Asim","doi":"10.1080/02664763.2024.2414357","DOIUrl":"10.1080/02664763.2024.2414357","url":null,"abstract":"Missing data is a common problem in many domains that rely on data analysis. The k Nearest Neighbors imputation method has been widely used to address this issue, but it has limitations in accurately imputing missing values, especially for datasets with small pairwise correlations and small values of k. In this study, we proposed a method, Ranked k Nearest Neighbors imputation that uses a similar approach to k Nearest Neighbor, but utilizing the concept of Ranked set sampling to select the most relevant neighbors for imputation. Our results show that the proposed method outperforms the standard k nearest neighbor method in terms of imputation accuracy both in case of Missing Completely at Random and Missing at Random mechanism, as demonstrated by consistently lower MSIE and MAIE values across all datasets. This suggests that the proposed method is a promising alternative for imputing missing values in datasets with small pairwise correlations and small values of k. Thus, the proposed Ranked k Nearest Neighbor method has important implications for data imputation in various domains and can contribute to the development of more efficient and accurate imputation methods without adding any computational complexity to an algorithm.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1103-1127"},"PeriodicalIF":1.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752879","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