Pub Date : 2026-05-01Epub Date: 2025-11-08DOI: 10.1016/j.jspi.2025.106360
Yuze Yuan , Shuyu Liu , Rongmao Zhang
Zhang and Chan (2021) considered the augmented Dickey–Fuller (ADF) test for an unit root process with linear noise driven by generalized autoregressive conditional heteroskedasticity (GARCH), and showed that the ADF test may perform even worse than the Dickey–Fuller test. The main reason is that the parameters of the lag terms in the ADF regression cannot be estimated consistently for infinite variance GARCH noises based on least square estimation (LSE). In this paper, we propose a self-weighted least square estimation (SWLSE) procedure to solve this problem. Consequently, a new test based on SWLSE for the unit-root is also proposed. It is shown that the SWLSE are consistent, and the proposed test converges to a functional of a stable process and a Brownian motion and performs well in term of size and power. Simulation study is conducted to evaluate the performance of our procedure, and a real-world illustrative example is provided.
{"title":"Self-weighted estimation for nonstationary processes with infinite variance GARCH errors","authors":"Yuze Yuan , Shuyu Liu , Rongmao Zhang","doi":"10.1016/j.jspi.2025.106360","DOIUrl":"10.1016/j.jspi.2025.106360","url":null,"abstract":"<div><div>Zhang and Chan (2021) considered the augmented Dickey–Fuller (ADF) test for an unit root process with linear noise driven by generalized autoregressive conditional heteroskedasticity (GARCH), and showed that the ADF test may perform even worse than the Dickey–Fuller test. The main reason is that the parameters of the lag terms in the ADF regression cannot be estimated consistently for infinite variance GARCH noises based on least square estimation (LSE). In this paper, we propose a self-weighted least square estimation (SWLSE) procedure to solve this problem. Consequently, a new test based on SWLSE for the unit-root is also proposed. It is shown that the SWLSE are consistent, and the proposed test converges to a functional of a stable process and a Brownian motion and performs well in term of size and power. Simulation study is conducted to evaluate the performance of our procedure, and a real-world illustrative example is provided.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106360"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-11-12DOI: 10.1016/j.jspi.2025.106361
Weiwei Zhuang , Weiqi Yang , Wenchen Liao , Yukun Liu
Lorenz dominance is a fundamental tool for assessing whether wealth or income disparity is greater in one population than another. Based on the well-established density ratio model, we propose a new semiparametric test for Lorenz dominance. We show that the limiting distribution of the proposed test statistic is the supremum of a Gaussian process. To facilitate practical application, we devise a bootstrap procedure to calculate the -value and establish its theoretical validity. Our simulation studies demonstrate that the proposed test correctly controls the Type I error and outperforms its competitors in terms of statistical power. Finally, we apply the test to compare salary distributions among higher education employees in Ohio from 2011 to 2015.
{"title":"Semiparametric tests for Lorenz dominance based on density ratio model","authors":"Weiwei Zhuang , Weiqi Yang , Wenchen Liao , Yukun Liu","doi":"10.1016/j.jspi.2025.106361","DOIUrl":"10.1016/j.jspi.2025.106361","url":null,"abstract":"<div><div>Lorenz dominance is a fundamental tool for assessing whether wealth or income disparity is greater in one population than another. Based on the well-established density ratio model, we propose a new semiparametric test for Lorenz dominance. We show that the limiting distribution of the proposed test statistic is the supremum of a Gaussian process. To facilitate practical application, we devise a bootstrap procedure to calculate the <span><math><mi>p</mi></math></span>-value and establish its theoretical validity. Our simulation studies demonstrate that the proposed test correctly controls the Type I error and outperforms its competitors in terms of statistical power. Finally, we apply the test to compare salary distributions among higher education employees in Ohio from 2011 to 2015.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106361"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-09-30DOI: 10.1016/j.jspi.2025.106355
Yiping Yang , Peixin Zhao
To address the challenges of variable selection in panel data models with fixed effects and varying coefficients, we introduce a novel method that combines basis function approximations with group nonconcave penalty functions. By utilizing a forward orthogonal deviation transformation, we eliminate fixed effects, allowing us to select significant variables and estimate non-zero coefficient functions. Under certain regularity conditions, we demonstrate that our method consistently identifies the true model structure, and the resulting estimators exhibit oracle properties. For computational efficiency, we have developed a group gradient descent algorithm that incorporates a transformation of the penalty terms. Simulation studies reveal that nonconvex penalties (SCAD/MCP) outperform the Lasso across various performance metrics. Furthermore, compared to existing methods, our approach significantly reduces false positives (FPs). To demonstrate the practical applicability and effectiveness of our method, we present an analysis of a real dataset.
{"title":"Variable selection in high-dimensional varying coefficient panel data models with fixed effects","authors":"Yiping Yang , Peixin Zhao","doi":"10.1016/j.jspi.2025.106355","DOIUrl":"10.1016/j.jspi.2025.106355","url":null,"abstract":"<div><div>To address the challenges of variable selection in panel data models with fixed effects and varying coefficients, we introduce a novel method that combines basis function approximations with group nonconcave penalty functions. By utilizing a forward orthogonal deviation transformation, we eliminate fixed effects, allowing us to select significant variables and estimate non-zero coefficient functions. Under certain regularity conditions, we demonstrate that our method consistently identifies the true model structure, and the resulting estimators exhibit oracle properties. For computational efficiency, we have developed a group gradient descent algorithm that incorporates a transformation of the penalty terms. Simulation studies reveal that nonconvex penalties (SCAD/MCP) outperform the Lasso across various performance metrics. Furthermore, compared to existing methods, our approach significantly reduces false positives (FPs). To demonstrate the practical applicability and effectiveness of our method, we present an analysis of a real dataset.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106355"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-11-01DOI: 10.1016/j.jspi.2025.106359
Yu Shi , Grace Y. Yi
Graphical models are powerful tools for characterizing conditional dependence structures among variables with complex relationships. Although many methods have been developed under the graphical modeling framework, their validity often hinges on the quality of the data. A fundamental assumption in most existing approaches is that all variables are measured precisely, an assumption frequently violated in practice. In many applications, mismeasurement of mixed discrete and continuous variables is a common challenge. In this paper, we address error-contaminated data involving both continuous and discrete variables by proposing a mixed latent Gaussian copula graphical measurement error model. To perform inference, we develop a simulation-based expectation–maximization procedure that explicitly accounts for mismeasurement effects. We further introduce a computationally efficient refinement to reduce the computational burden. Asymptotic properties of the proposed estimator are established, and its finite-sample performance is evaluated through numerical studies.
{"title":"Mixed latent graphical models with mixed measurement error and misclassification in variables","authors":"Yu Shi , Grace Y. Yi","doi":"10.1016/j.jspi.2025.106359","DOIUrl":"10.1016/j.jspi.2025.106359","url":null,"abstract":"<div><div>Graphical models are powerful tools for characterizing conditional dependence structures among variables with complex relationships. Although many methods have been developed under the graphical modeling framework, their validity often hinges on the quality of the data. A fundamental assumption in most existing approaches is that all variables are measured precisely, an assumption frequently violated in practice. In many applications, mismeasurement of mixed discrete and continuous variables is a common challenge. In this paper, we address error-contaminated data involving both continuous and discrete variables by proposing a mixed latent Gaussian copula graphical measurement error model. To perform inference, we develop a simulation-based expectation–maximization procedure that explicitly accounts for mismeasurement effects. We further introduce a computationally efficient refinement to reduce the computational burden. Asymptotic properties of the proposed estimator are established, and its finite-sample performance is evaluated through numerical studies.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106359"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-09-12DOI: 10.1016/j.jspi.2025.106340
Torsten Glemser, Rainer Schwabe
The goal of subsampling is to select an informative subset of all observations, when using the full data for statistical analysis is not viable. We construct locally -optimal subsampling designs under a Poisson regression model with a log link in one covariate. A representation of the support of locally -optimal subsampling designs is established. We make statements on scale-location transformations of the covariate that require a simultaneous transformation of the regression parameter. The performance of the methods is demonstrated by illustrating examples. To show the advantage of the optimal subsampling designs, we examine the efficiency of uniform random subsampling as well as of two heuristic designs. Further, the efficiency of locally -optimal subsampling designs is studied when the parameter is misspecified.
{"title":"D-criterion based optimal subsampling in Poisson regression with one covariate","authors":"Torsten Glemser, Rainer Schwabe","doi":"10.1016/j.jspi.2025.106340","DOIUrl":"10.1016/j.jspi.2025.106340","url":null,"abstract":"<div><div>The goal of subsampling is to select an informative subset of all observations, when using the full data for statistical analysis is not viable. We construct locally <span><math><mi>D</mi></math></span>-optimal subsampling designs under a Poisson regression model with a log link in one covariate. A representation of the support of locally <span><math><mi>D</mi></math></span>-optimal subsampling designs is established. We make statements on scale-location transformations of the covariate that require a simultaneous transformation of the regression parameter. The performance of the methods is demonstrated by illustrating examples. To show the advantage of the optimal subsampling designs, we examine the efficiency of uniform random subsampling as well as of two heuristic designs. Further, the efficiency of locally <span><math><mi>D</mi></math></span>-optimal subsampling designs is studied when the parameter is misspecified.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106340"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-08-27DOI: 10.1016/j.jspi.2025.106336
Pengfei Liu , Kai Lou , Yangchun Zhang , Peng Zhao , Wang Zhou
In recent years, a substantial of biomarkers have surfaced to facilitate the prompt diagnosis and intervention of chronic kidney disease. However, the lack of a reliable approach to compare biomarker efficacy poses a significant challenge in clinical practice and biomedical research. The inability to accurately assess biomarkers’ performance limits their utility in disease diagnosis. In this article, we study the efficiency of different diagnostic markers by comparing the areas under the receiver operating characteristic curves of markers, which are estimated via the Wilcoxon–Mann–Whitney statistics. Furthermore, the precision of interval estimation was enhanced through the implementation of the Edgeworth expansion and bootstrap approximation on the statistics. By performing numerical simulations, we have demonstrated that our improved methods exhibit superior accuracy in constructing confidence intervals when compared to the traditional normal approximation method.
{"title":"Evaluation of diagnostic biomarkers: A comparative analysis by area under the receiver operating characteristic curve","authors":"Pengfei Liu , Kai Lou , Yangchun Zhang , Peng Zhao , Wang Zhou","doi":"10.1016/j.jspi.2025.106336","DOIUrl":"10.1016/j.jspi.2025.106336","url":null,"abstract":"<div><div>In recent years, a substantial of biomarkers have surfaced to facilitate the prompt diagnosis and intervention of chronic kidney disease. However, the lack of a reliable approach to compare biomarker efficacy poses a significant challenge in clinical practice and biomedical research. The inability to accurately assess biomarkers’ performance limits their utility in disease diagnosis. In this article, we study the efficiency of different diagnostic markers by comparing the areas under the receiver operating characteristic curves of markers, which are estimated via the Wilcoxon–Mann–Whitney statistics. Furthermore, the precision of interval estimation was enhanced through the implementation of the Edgeworth expansion and bootstrap approximation on the statistics. By performing numerical simulations, we have demonstrated that our improved methods exhibit superior accuracy in constructing confidence intervals when compared to the traditional normal approximation method.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106336"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-09-25DOI: 10.1016/j.jspi.2025.106353
Kiyoshi Inoue
In this paper, we consider the joint distribution of numbers of occurrences of countably many runs of several lengths in a sequence of nonnegative integer valued independent and identically distributed random variables through the generating functions. We propose a generalization of the potential partition polynomials, which gives effective computational tools for the derivation of probability functions. The waiting time problems associated with infinitely many runs are investigated and formulae for the evaluation of the generating functions are given. The results presented here provide a wide framework for developing the multivariate distribution theory of runs. Finally, we discuss several applications and numerical examples to show how our theoretical results are applied to the investigation of runs, as well as parameter estimation problems.
{"title":"Joint distribution of numbers of occurrences of countably many runs of specified lengths in a sequence of discrete random variables","authors":"Kiyoshi Inoue","doi":"10.1016/j.jspi.2025.106353","DOIUrl":"10.1016/j.jspi.2025.106353","url":null,"abstract":"<div><div>In this paper, we consider the joint distribution of numbers of occurrences of countably many runs of several lengths in a sequence of nonnegative integer valued independent and identically distributed random variables through the generating functions. We propose a generalization of the potential partition polynomials, which gives effective computational tools for the derivation of probability functions. The waiting time problems associated with infinitely many runs are investigated and formulae for the evaluation of the generating functions are given. The results presented here provide a wide framework for developing the multivariate distribution theory of runs. Finally, we discuss several applications and numerical examples to show how our theoretical results are applied to the investigation of runs, as well as parameter estimation problems.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106353"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Causal inference tools, in particular those of variance decomposition, hierarchical data structures and counterfactuals, are applied to the study of the methodology of dose-finding studies in oncology. A detailed variance decomposition brings into a much sharper focus the relative performance of different designs. We develop and present new results on the role played by the order of patient inclusions into a sequential dose-finding study. These results make it clear why, previously, authors could easily be misled into a conclusion that different designs enjoy similar performances. This is not so and we show how to avoid making that mistake. We highlight our findings via both theoretical and numerical studies.
{"title":"Causal inference in early phase clinical trials: Variance decomposition and order of patient inclusion","authors":"Matthieu Clertant , Meliha Akouba , Alexia Iasonos , John O’Quigley","doi":"10.1016/j.jspi.2025.106352","DOIUrl":"10.1016/j.jspi.2025.106352","url":null,"abstract":"<div><div>Causal inference tools, in particular those of variance decomposition, hierarchical data structures and counterfactuals, are applied to the study of the methodology of dose-finding studies in oncology. A detailed variance decomposition brings into a much sharper focus the relative performance of different designs. We develop and present new results on the role played by the order of patient inclusions into a sequential dose-finding study. These results make it clear why, previously, authors could easily be misled into a conclusion that different designs enjoy similar performances. This is not so and we show how to avoid making that mistake. We highlight our findings via both theoretical and numerical studies.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106352"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-09-13DOI: 10.1016/j.jspi.2025.106339
Fei Ye , Jingsong Xiao , Weidong Ma , Yulai Miao , Ying Yang
The stochastic blockmodel (SBM) is a widely used model for representing graphs. Numerous approaches have been applied to the SBM to detect latent community structures in graphs, typically using two types of consistency (strong and weak) to evaluate their performance. Most of these methods have been studied and shown to be consistent under the SBM framework. However, the consistency of the weighted SBM, an important extension of the SBM, has been largely overlooked. Moreover, few approaches are capable of detecting communities when the number of communities is unknown. In this paper, we propose a nonparametric method for effective community detection under the assortative, nonparametric weighted SBM with an unknown number of communities, and we establish the consistency of our approach. We introduce a novel concept, “consistency in relationship”, as a more practical criterion to assess the performance of community detection algorithms. Since solving the optimization problem in our approach becomes intractable for large sample sizes, we propose an efficient algorithm to approximate it. Simulations demonstrate that our community detection method is both efficient and robust, particularly for unbalanced networks. We illustrate the effectiveness of our approach on three real-world networks.
{"title":"Consistent community detection approach in the nonparametric weighted stochastic blockmodel with unspecified number of communities","authors":"Fei Ye , Jingsong Xiao , Weidong Ma , Yulai Miao , Ying Yang","doi":"10.1016/j.jspi.2025.106339","DOIUrl":"10.1016/j.jspi.2025.106339","url":null,"abstract":"<div><div>The stochastic blockmodel (SBM) is a widely used model for representing graphs. Numerous approaches have been applied to the SBM to detect latent community structures in graphs, typically using two types of consistency (strong and weak) to evaluate their performance. Most of these methods have been studied and shown to be consistent under the SBM framework. However, the consistency of the weighted SBM, an important extension of the SBM, has been largely overlooked. Moreover, few approaches are capable of detecting communities when the number of communities is unknown. In this paper, we propose a nonparametric method for effective community detection under the assortative, nonparametric weighted SBM with an unknown number of communities, and we establish the consistency of our approach. We introduce a novel concept, “consistency in relationship”, as a more practical criterion to assess the performance of community detection algorithms. Since solving the optimization problem in our approach becomes intractable for large sample sizes, we propose an efficient algorithm to approximate it. Simulations demonstrate that our community detection method is both efficient and robust, particularly for unbalanced networks. We illustrate the effectiveness of our approach on three real-world networks.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106339"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2025-10-30DOI: 10.1016/j.jspi.2025.106357
Yuliang Zhou, Qianqian Zhao, Shengli Zhao
Sliced designs are widely used in multi-platform experiments. A sliced design contains several sub-designs divided by the sliced factor, and each sub-design is assigned to a platform, respectively. In some experimental scenarios, it is necessary to consider the optimality of both the sub-designs and the complete sliced designs, such sliced designs are referred to as general sliced (GS) designs. To construct the optimal GS designs for such scenarios, we propose the general sliced effect hierarchy principle (GSEHP). Based on the GSEHP, we introduce the general sliced minimum aberration (GSMA) criterion and choose the GSMA designs as optimal GS designs when the sliced factor and design factors are equally important. Some GSMA designs with 32 and 64 runs are tabulated. Additionally, we present a practical example to illustrate the application of GSMA designs in guiding strategies of webpage setting on two platforms.
{"title":"General sliced minimum aberration designs for multi-platform experiments","authors":"Yuliang Zhou, Qianqian Zhao, Shengli Zhao","doi":"10.1016/j.jspi.2025.106357","DOIUrl":"10.1016/j.jspi.2025.106357","url":null,"abstract":"<div><div>Sliced designs are widely used in multi-platform experiments. A sliced design contains several sub-designs divided by the sliced factor, and each sub-design is assigned to a platform, respectively. In some experimental scenarios, it is necessary to consider the optimality of both the sub-designs and the complete sliced designs, such sliced designs are referred to as general sliced (GS) designs. To construct the optimal GS designs for such scenarios, we propose the general sliced effect hierarchy principle (GSEHP). Based on the GSEHP, we introduce the general sliced minimum aberration (GSMA) criterion and choose the GSMA designs as optimal GS designs when the sliced factor and design factors are equally important. Some GSMA designs with 32 and 64 runs are tabulated. Additionally, we present a practical example to illustrate the application of GSMA designs in guiding strategies of webpage setting on two platforms.</div></div>","PeriodicalId":50039,"journal":{"name":"Journal of Statistical Planning and Inference","volume":"242 ","pages":"Article 106357"},"PeriodicalIF":0.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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