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Statistical Theory and Related Fields最新文献

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Target toxicity design for phase I dose-finding I期剂量测定的靶毒性设计
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-08-13 DOI: 10.1080/24754269.2020.1800331
Wenchuan Guo, B. Zhong
{"title":"Target toxicity design for phase I dose-finding","authors":"Wenchuan Guo, B. Zhong","doi":"10.1080/24754269.2020.1800331","DOIUrl":"https://doi.org/10.1080/24754269.2020.1800331","url":null,"abstract":"We propose a new two-/three-stage dose-finding design called Target Toxicity (TT) for phase I clinical trials, where we link the decision rules in the dose-finding process with the conclusions from a hypothesis test. The power to detect excessive toxicity is also given. This solves the problem of why the minimal number of patients is needed for the selected dose level. Our method provides a statistical explanation of traditional ‘3+3’ design using frequentist framework. The proposed method is very flexible and it incorporates other interval-based decision rules through different parameter settings. We provide the decision tables to guide investigators when to decrease, increase or repeat a dose for next cohort of subjects. Simulation experiments were conducted to compare the performance of the proposed method with other dose-finding designs. A free open source R package tsdf is available on CRAN. It is dedicated to deriving two-/three-stage design decision tables and perform dose-finding simulations.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"149 - 161"},"PeriodicalIF":0.5,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1800331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46466841","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
Covariate balancing based on kernel density estimates for controlled experiments 基于核密度估计的受控实验协变量平衡
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-08-12 DOI: 10.1080/24754269.2021.1878742
Yiou Li, Lulu Kang, Xiao Huang
{"title":"Covariate balancing based on kernel density estimates for controlled experiments","authors":"Yiou Li, Lulu Kang, Xiao Huang","doi":"10.1080/24754269.2021.1878742","DOIUrl":"https://doi.org/10.1080/24754269.2021.1878742","url":null,"abstract":"ABSTRACT Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomised design is usually used to randomly assign treatment levels to experimental units. When covariates of the experimental units are available, the experimental design should achieve covariate balancing among the treatment groups, such that the statistical inference of the treatment effects is not confounded with any possible effects of covariates. However, covariate imbalance often exists, because the experiment is carried out based on a single realisation of the complete randomisation. It is more likely to occur and worsen when the size of the experimental units is small or moderate. In this paper, we introduce a new covariate balancing criterion, which measures the differences between kernel density estimates of the covariates of treatment groups. To achieve covariate balance before the treatments are randomly assigned, we partition the experimental units by minimising the criterion, then randomly assign the treatment levels to the partitioned groups. Through numerical examples, we show that the proposed partition approach can improve the accuracy of the difference-in-mean estimator and outperforms the complete randomisation and rerandomisation approaches.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"102 - 113"},"PeriodicalIF":0.5,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2021.1878742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41800210","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
A three-parameter logistic regression model 一个三参数逻辑回归模型
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-24 DOI: 10.1080/24754269.2020.1796098
Xiaoli Yu, Shaoting Li, Jiahua Chen
{"title":"A three-parameter logistic regression model","authors":"Xiaoli Yu, Shaoting Li, Jiahua Chen","doi":"10.1080/24754269.2020.1796098","DOIUrl":"https://doi.org/10.1080/24754269.2020.1796098","url":null,"abstract":"Dose–response experiments and data analyses are often carried out according to an optimal design under a model assumption. A two-parameter logistic model is often used because of its nice mathematical properties and plausible stochastic response mechanisms. There is an extensive literature on its optimal designs and data analysis strategies. However, a model is at best a good approximation in a real-world application, and researchers must be aware of the risk of model mis-specification. In this paper, we investigate the effectiveness of the sequential ED-design, the D-optimal design, and the up-and-down design under the three-parameter logistic regression model, and we develop a numerical method for the parameter estimation. Simulations show that the combination of the proposed model and the data analysis strategy performs well. When the logistic model is correct, this more complex model has hardly any efficiency loss. The three-parameter logistic model works better than the two-parameter logistic model in the presence of model mis-specification.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"5 1","pages":"265 - 274"},"PeriodicalIF":0.5,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1796098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46183255","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
Empirical likelihood estimation in multivariate mixture models with repeated measurements 具有重复测量的多元混合模型的经验似然估计
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2019.1630544
Yuejiao Fu, Yukun Liu, Hsiao‐Hsuan Wang, Xiaogang Wang
{"title":"Empirical likelihood estimation in multivariate mixture models with repeated measurements","authors":"Yuejiao Fu, Yukun Liu, Hsiao‐Hsuan Wang, Xiaogang Wang","doi":"10.1080/24754269.2019.1630544","DOIUrl":"https://doi.org/10.1080/24754269.2019.1630544","url":null,"abstract":"Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions. In such situations, the main interest may be not only in estimating the component parameters, but also in obtaining reliable estimates of the mixing proportions. In this paper, we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model. The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"152 - 160"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2019.1630544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41695103","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: 4
Forecasting semi-stationary processes and statistical arbitrage 预测半平稳过程与统计套利
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2019.1675420
S. Bao, Shi Chen, W. Zheng, Yu Zhou
{"title":"Forecasting semi-stationary processes and statistical arbitrage","authors":"S. Bao, Shi Chen, W. Zheng, Yu Zhou","doi":"10.1080/24754269.2019.1675420","DOIUrl":"https://doi.org/10.1080/24754269.2019.1675420","url":null,"abstract":"ABSTRACT If a financial derivative can be traded consecutively and its terminal payoffs can be adjusted as the sum of a bounded process and a stationary process, then we can use the moving average of the historical payoffs to forecast and the corresponding errors form a generalised mean reversion process. Thus we can price the financial derivatives by its moving average. One can even possibly get statistical arbitrage from certain derivative pricing. We particularly discuss the example of European call options. We show that there is a possibility to get statistical arbitrage from Black–Scholes's option price.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"179 - 189"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2019.1675420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49065565","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: 1
Discussion on ‘Review of sparse sufficient dimension reduction’ 关于“稀疏充分降维综述”的讨论
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2020.1829393
Xin Zhang
{"title":"Discussion on ‘Review of sparse sufficient dimension reduction’","authors":"Xin Zhang","doi":"10.1080/24754269.2020.1829393","DOIUrl":"https://doi.org/10.1080/24754269.2020.1829393","url":null,"abstract":"I congratulate the authors on an excellent overview of an important research area. Sufficient dimension reduction methods are based on the model-free driving condition that Y ⫫ X ∣ P S X , where X ...","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"146 - 148"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1829393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46235760","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
Comment on ‘Review of sparse sufficient dimension reduction’ 对“稀疏充分降维的回顾”的评论
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2020.1829394
M. Power, Yuexiao Dong
{"title":"Comment on ‘Review of sparse sufficient dimension reduction’","authors":"M. Power, Yuexiao Dong","doi":"10.1080/24754269.2020.1829394","DOIUrl":"https://doi.org/10.1080/24754269.2020.1829394","url":null,"abstract":"We congratulate the authors on a very interesting overview of sparse sufficient dimension reduction (SDR). Sparse SDR methods are discussed in both the classical n>p setting as well as the high-dim...","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"149 - 150"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1829394","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47324188","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
Quantile treatment effect estimation with dimension reduction 降维的分位数治疗效果估计
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2019.1696645
Ying Zhang, Lei Wang, Menggang Yu, Jun Shao
{"title":"Quantile treatment effect estimation with dimension reduction","authors":"Ying Zhang, Lei Wang, Menggang Yu, Jun Shao","doi":"10.1080/24754269.2019.1696645","DOIUrl":"https://doi.org/10.1080/24754269.2019.1696645","url":null,"abstract":"Quantile treatment effects can be important causal estimands in evaluation of biomedical treatments or interventions for health outcomes such as medical cost and utilisation. We consider their estimation in observational studies with many possible covariates under the assumption that treatment and potential outcomes are independent conditional on all covariates. To obtain valid and efficient treatment effect estimators, we replace the set of all covariates by lower dimensional sets for estimation of the quantiles of potential outcomes. These lower dimensional sets are obtained using sufficient dimension reduction tools and are outcome specific. We justify our choice from efficiency point of view. We prove the asymptotic normality of our estimators and our theory is complemented by some simulation results and an application to data from the University of Wisconsin Health Accountable Care Organization.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"202 - 213"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2019.1696645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48546466","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
Efficient GMM estimation with singular system of moment conditions 奇异系统矩条件下的有效GMM估计
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2019.1653159
Zhiguo Xiao
{"title":"Efficient GMM estimation with singular system of moment conditions","authors":"Zhiguo Xiao","doi":"10.1080/24754269.2019.1653159","DOIUrl":"https://doi.org/10.1080/24754269.2019.1653159","url":null,"abstract":"Standard generalised method of moments (GMM) estimation was developed for nonsingular system of moment conditions. However, many important economic models are characterised by singular system of moment conditions. This paper shows that efficient GMM estimation of such models can be achieved by using the reflexive generalised inverses, in particular the Moore–Penrose generalised inverse, of the variance matrix of the sample moment conditions as the weighting matrix. We provide a consistent estimator of the optimal weighting matrix and establish its consistency. Potential issues of using generalised inverse and some remedies are also discussed.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"172 - 178"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2019.1653159","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41766386","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: 3
A selective overview of sparse sufficient dimension reduction 稀疏充分降维的一个选择性综述
IF 0.5
Statistical Theory and Related Fields Pub Date : 2020-07-02 DOI: 10.1080/24754269.2020.1829389
Lu Li, Xuerong Meggie Wen, Zhou Yu
{"title":"A selective overview of sparse sufficient dimension reduction","authors":"Lu Li, Xuerong Meggie Wen, Zhou Yu","doi":"10.1080/24754269.2020.1829389","DOIUrl":"https://doi.org/10.1080/24754269.2020.1829389","url":null,"abstract":"High-dimensional data analysis has been a challenging issue in statistics. Sufficient dimension reduction aims to reduce the dimension of the predictors by replacing the original predictors with a minimal set of their linear combinations without loss of information. However, the estimated linear combinations generally consist of all of the variables, making it difficult to interpret. To circumvent this difficulty, sparse sufficient dimension reduction methods were proposed to conduct model-free variable selection or screening within the framework of sufficient dimension reduction. We review the current literature of sparse sufficient dimension reduction and do some further investigation in this paper.","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"4 1","pages":"121 - 133"},"PeriodicalIF":0.5,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24754269.2020.1829389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49290169","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: 4
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