The Annals of Applied Statistics最新文献_第6页

Building a dose toxo-equivalence model from a Bayesian meta-analysis of published clinical trials 通过对已发表的临床试验进行贝叶斯荟萃分析，建立剂量毒性等效模型

The Annals of Applied Statistics Pub Date : 2023-12-01 DOI: 10.1214/23-aoas1748

E. Sigworth, Samuel M. Rubinstein, Jeremy L. Warner, Yong Chen, Qingxia Chen

引用次数: 0

A reluctant additive model framework for interpretable nonlinear individualized treatment rules 可解释非线性个体化治疗规则的勉强加法模型框架

The Annals of Applied Statistics Pub Date : 2023-11-02 DOI: 10.1214/23-AOAS1767

Jacob M. Maronge, J. Huling, Guanhua Chen

{"title":"A reluctant additive model framework for interpretable nonlinear individualized treatment rules","authors":"Jacob M. Maronge, J. Huling, Guanhua Chen","doi":"10.1214/23-AOAS1767","DOIUrl":"https://doi.org/10.1214/23-AOAS1767","url":null,"abstract":"Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treatment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optimal ITRs are nonlinear, or black-box nonlinear ITRs, which may be hard to interpret and can be overly complex. This dilemma indicates a tension between interpretability and accuracy of treatment decisions. Here we propose an additive model-based nonlinear ITR learning method that balances interpretability and flexibility of the ITR. Our approach aims to strike this balance by allowing both linear and nonlinear terms of the covariates in the final ITR. Our approach is parsimonious in that the nonlinear term is included in the final ITR only when it substantially improves the ITR performance. To prevent overfitting, we combine cross-fitting and a specialized information criterion for model selection. Through extensive simulations, we show that our methods are data-adaptive to the degree of nonlinearity and can favorably balance ITR interpretability and flexibility. We further demonstrate the robust performance of our methods with an application to a cancer drug sensitive study.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"10 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial quantile autoregression for season within year daily maximum temperature data 季节内日最高气温数据的空间分位数自回归

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1719

Jorge Castillo-Mateo, J. Asín, A. Cebrián, A. Gelfand, J. Abaurrea

{"title":"Spatial quantile autoregression for season within year daily maximum temperature data","authors":"Jorge Castillo-Mateo, J. Asín, A. Cebrián, A. Gelfand, J. Abaurrea","doi":"10.1214/22-aoas1719","DOIUrl":"https://doi.org/10.1214/22-aoas1719","url":null,"abstract":"Regression is the most widely used modeling tool in statistics. Quantile regression offers a strategy for enhancing the regression picture beyond custom-ary mean regression. With time series data, we move to quantile autoregression and, ﬁnally, with spatially referenced time series, we move to space-time quantile regression. Here, we are concerned with the spatio-temporal evolution of daily maximum temperature, particularly with regard to extreme heat. Our motivating dataset is 60 years of daily summer maximum temperature data over Aragón in Spain. Hence, we work with time on two scales—days within summer season across years—collected at geo-coded station locations. For a speciﬁed quantile, we ﬁt a very ﬂexible mixed effects autoregressive model, introducing four spatial processes. We work with asymmetric Laplace errors to take advantage of the available conditional Gaussian representation for these distributions. Further, while the auto-regressive model yields conditional quantiles, we demonstrate how to extract marginal quantiles with the asymmetric Laplace speciﬁcation. Thus, we are able to interpolate quantiles for any days within years across our study region.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"96 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123577995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Bayesian modeling of interaction between features in sparse multivariate count data with application to microbiome study 稀疏多变量计数数据特征间交互作用的贝叶斯建模及其在微生物组研究中的应用

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1690

Shuangjie Zhang, Yuning Shen, Irene A. Chen, Juhee Lee

{"title":"Bayesian modeling of interaction between features in sparse multivariate count data with application to microbiome study","authors":"Shuangjie Zhang, Yuning Shen, Irene A. Chen, Juhee Lee","doi":"10.1214/22-aoas1690","DOIUrl":"https://doi.org/10.1214/22-aoas1690","url":null,"abstract":"Many statistical methods have been developed for the analysis of microbial community profiles, but due to the complexity of typical microbiome measurements, inference of interactions between microbial features remains challenging. We develop a Bayesian zero-inflated rounded log-normal kernel method to model interaction between microbial features in a community using multivariate count data in the presence of covariates and excess zeros. The model carefully constructs the interaction structure by imposing joint sparsity on the covariance matrix of the kernel and obtains a reliable estimate of the structure with a small sample size. The model also includes zero inflation to account for excess zeros observed in data and infers differential abundance of microbial features associated with covariates through log-linear regression. We provide simulation studies and real data analysis examples to demonstrate the developed model. Comparison of the model to a simpler model and popular alternatives in simulation studies shows that in addition to an added and important insight on the feature interaction, it yields superior parameter estimates and model fit in various settings.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128573699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penalized estimating equations for generalized linear models with multiple imputation 多重插值广义线性模型的惩罚估计方程

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1721

Yang Li, Haoyu Yang, Haochen Yu, Hanwen Huang, Ye Shen

{"title":"Penalized estimating equations for generalized linear models with multiple imputation","authors":"Yang Li, Haoyu Yang, Haochen Yu, Hanwen Huang, Ye Shen","doi":"10.1214/22-aoas1721","DOIUrl":"https://doi.org/10.1214/22-aoas1721","url":null,"abstract":"Missing values among variables present a challenge in variable selection in the generalized linear model. Common strategies that delete observations with missing information may cause serious information loss. Multiple imputation has been widely used in recent years because it provides unbiased statistical results given a correctly speciﬁed imputation model and considers the uncertainty of the missing data. However, variable selection methods in the generalized linear model with multiply imputed data have not yet been studied widely. In this study, we introduce penalized estimating equations for generalized linear models with multiple imputation (PEE–MI), which incorporates the correlation of multiple imputed observations into the objective function. The theoretical performance of the proposed PEE–MI depends on the penalized function adopted. We use the adaptive least absolute shrinkage and selection operator (adaptive LASSO) as an illustrating example. Simulations show that PEE–MI outperforms the alternatives. The proposed method is shown to select variables with clinical relevance when applied to a database of laboratory-diagnosed A/H7N9 patients in Zhejiang province, China.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130630186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Bayesian nested lasso for mixed frequency regression models 混合频率回归模型的贝叶斯套套

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1718

Satyajit Ghosh, K. Khare, G. Michailidis

{"title":"The Bayesian nested lasso for mixed frequency regression models","authors":"Satyajit Ghosh, K. Khare, G. Michailidis","doi":"10.1214/22-aoas1718","DOIUrl":"https://doi.org/10.1214/22-aoas1718","url":null,"abstract":"Even though many time series are sampled at diﬀerent frequencies, their joint evolution is usually modeled and analyzed at a common low frequency. The Mixed Data Sampling (MIDAS) framework was developed to enable joint modeling of mixed frequency tempo-rally evolving data, with GDP forecasting as a key motivating application. In this paper, we develop a fully Bayesian method to jointly estimate both the appropriate lag, as well as the regression coeﬃcients in linear models wherein the response is measured at a lower frequency than the predictors. This is accomplished through a novel prior distribution, coined the Bayesian Nested Lasso (BNL), that leads to principled selection of the lag of the predictors, reduces the eﬀective number of model parameters through sparsity induced by the lasso component and ﬁnally incorporates desirable decay patterns over time lags in the magnitude of the corresponding regression coeﬃcients. Further, it is easy to obtain samples from the posterior distribution due to the closed form expressions for the conditional distributions of the model parameters. Numerical results obtained from synthetic and macroeconomic data illustrate the good performance of the proposed Bayesian framework in parameter selection and estimation, and in the key task of GDP forecasting. How-ever, more timely and frequent forecasts would be highly beneﬁcial to both policy makers (central bankers, treasury oﬃcials) and other ﬁnancial market participants.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134319651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes 性别刻板印象时间动态的贝叶斯非参数混合模型

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1717

Maria De Iorio, Stefano Favaro, Alessandra Guglielmi, Lifeng Ye

{"title":"Bayesian nonparametric mixture modeling for temporal dynamics of gender stereotypes","authors":"Maria De Iorio, Stefano Favaro, Alessandra Guglielmi, Lifeng Ye","doi":"10.1214/22-aoas1717","DOIUrl":"https://doi.org/10.1214/22-aoas1717","url":null,"abstract":"The study of temporal dynamics of gender and ethnic stereotypes is an important topic in many disciplines at the intersection between statistics and social sciences. In this paper, we make use of word embeddings, a common tool in natural language processing, and of Bayesian nonparametric mixture modeling for the analysis of temporal dynamics of gender stereotypes in adjectives and occupation over the 20th and 21st centuries in the United States. Our Bayesian nonparametric approach relies on a novel dependent Dirichlet process prior, and it allows for both dynamic density estimation and dynamic clustering of adjective embedding and occupation embedding biases in a hierarchical setting. Posterior inference is performed through a particle Markov chain Monte Carlo algorithm which is simple and computationally efﬁcient. An application to time-dependent data for adjective embedding bias and for occupation embedding bias shows that our approach enables the quantiﬁca-tion of historical trends of gender stereotypes, and hence allows to identify how speciﬁc adjectives and occupations have become more closely associated with a female rather than male over time. our","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating GARCH(1,1) in the presence of missing data 缺失数据下GARCH(1,1)的估计

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/23-aoas1734

D. C. Wee, Feng Chen, William T. M. Dunsmuir

{"title":"Estimating GARCH(1,1) in the presence of missing data","authors":"D. C. Wee, Feng Chen, William T. M. Dunsmuir","doi":"10.1214/23-aoas1734","DOIUrl":"https://doi.org/10.1214/23-aoas1734","url":null,"abstract":"Maximum likelihood estimation of the famous GARCH(1,1) model is generally straightforward given the full observation series. However, when some observations are missing, the marginal likelihood of the observed data is intractable in most cases of interest. Also intractable is the likelihood from temporally aggregated data. For both these problems, we propose to approximate the intractable likelihoods through sequential Monte Carlo (SMC). The SMC approximation is done in a smooth manner so that the resulting approximate likelihoods can be numerically optimized to obtain parameter estimates. In the case with data aggregation, the use of SMC is made possible by a novel state space representation of the aggregated GARCH series. Through extensive simulation experiments, the proposed method is found to be computationally feasible and produce more accurate estimators of the model parameters compared with other recently published methods, especially in the case with aggregated data. In addition, the Hessian matrix of the minus logarithm of the approximate likelihood can be inverted to produce fairly accurate standard error estimates. The proposed methodology is applied to the analysis of time series data on several exchange-traded funds on the Australian Stock Exchange with missing prices due to interruptions such as scheduled trading holidays.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125080577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum Modeling biomarker ratios with gamma distributed components 用伽马分布成分建模生物标志物比率

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/23-aoas1777

M. Schmid

引用次数: 0

Bayesian additive regression trees for genotype by environment interaction models 环境相互作用模型基因型的贝叶斯加性回归树

The Annals of Applied Statistics Pub Date : 2023-09-01 DOI: 10.1214/22-aoas1698

Danilo A. Sarti, Estevão B. Prado, Alan N. Inglis, Antônia A. L. dos Santos, Catherine B. Hurley, Rafael A. Moral, Andrew C. Parnell

引用次数: 0