Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.Mor...Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.More recent approaches use predictiveness comparisons with flexible machine-learning model fitting procedures to yield algorithm-agnostic tests,yet they require large labeled samples.The authors introduce a prediction-powered,semi-supervised framework that:1)Imputes responses for unlabeled data via a pretrained model;2)Corrects imputation bias with a rectifier calibrated on labeled data;3)Adaptively balances these components through a data-driven power-tuning parameter.Building on algorithm-agnostic out-of-sample predictiveness comparisons,the proposed method integrates unlabeled information to enhance power.Theoretical analyses and numerical results demonstrate that the proposed test controls Type I error and substantially improves power over fully supervised counterparts,even under imputation-model misspecification.展开更多
This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This eva...This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.展开更多
The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pil...The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.展开更多
We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-...We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.展开更多
基金supported by the National Key R&D Program of China under Grant Nos.2022YFA1003800 and 2022YFA1003703the National Natural Science Foundation of China under Grant Nos.12531011,12231011 and 12471255+3 种基金the Natural Science Foundation of Shanghai under Grant No.23ZR1419400the Fundamental Research Funds for the Central Universities under Grant No.63253110supported by China Postdoctoral Science Foundation General Funding Program under Grant No.2025M7730792025 Annual Planning Project of the Commerce Statistical Society of China under Grant No.2025STY115。
文摘Model checking evaluates whether a statistical model faithfully captures the underlying data-generating process.Classical tests—such as local-smoothing and empirical-process methods—break down in high dimensions.More recent approaches use predictiveness comparisons with flexible machine-learning model fitting procedures to yield algorithm-agnostic tests,yet they require large labeled samples.The authors introduce a prediction-powered,semi-supervised framework that:1)Imputes responses for unlabeled data via a pretrained model;2)Corrects imputation bias with a rectifier calibrated on labeled data;3)Adaptively balances these components through a data-driven power-tuning parameter.Building on algorithm-agnostic out-of-sample predictiveness comparisons,the proposed method integrates unlabeled information to enhance power.Theoretical analyses and numerical results demonstrate that the proposed test controls Type I error and substantially improves power over fully supervised counterparts,even under imputation-model misspecification.
基金supported by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Educationsupported by the National Natural Scientific Foundation of China under Grant No.NSFC12131006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Science under Grant No.CI2023C063YLL。
文摘This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.
文摘The proliferation of high-dimensional data and the widespread use of complex models present central challenges in contemporary statistics and data science.Dimension reduction and model checking,as two foundational pillars supporting scientific inference and data-driven decisionmaking,have evolved through the collective wisdom of generations of statisticians.This special issue,titled"Recent Developments in Dimension Reduction and Model Checking for regressions",not only aims to showcase cutting-edge advances in the field but also carries a distinct sense of academic homage to honor the groundbreaking and enduring contributions of Professor Lixing Zhu,a leading scholar whose work has profoundly shaped both areas.
基金supported in part by the National Key R&D Program of China (Contract Nos.2023YFA1606500,2024YFE0109800,and 2024YFE0110400)Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB34010000)+5 种基金the Gansu Key Project of Science and Technology (Grant No.23ZDGA014)the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2021B0301030006)the National Natural Science Foundation of China (Grant Nos.12105328,W2412040,12475126,12422507,12035011,12375118,12435008,and W2412043)the Chinese Academy of Sciences Project for Young Scientists in Basic Research(Grant No.YSBR-002)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant Nos.2020409 and 2023439)the Russian Science Foundation (Grant No.25-42-00003)。
文摘We report the results of the experiment on synthesizing ^(287,288)Mc isotopes (Z=115) using the fusionevaporation reaction ^(243)Am(^(48)Ca,4n,3n)^(287,288)Mc at the Spectrometer for Heavy Atoms and Nuclear Structure-2(SHANS2),a gas-filled recoil separator located at the China Accelerator Facility for Superheavy Elements(CAFE2).In total,20 decay chains are attributed to ^(288)Mc and 1 decay chain is assigned to ^(287)Mc.The measured oa-decay properties of ^(287,288)Mc as well as its descendants are consistent with the known data.No additional decay chains originating from the 2n or 5n reaction channels were detected.The excitation function of the ^(243)Am(^(48)Ca,3n)^(288)Mc reaction was measured at the cross-section level of picobarn,which indicates the promising capability for the study of heavy and superheavy nuclei at the facility.
