Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model include...Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each stellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey(SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates a superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.展开更多
The performances of preliminary test estimators for error variance based on W,LR and LM tests in a normal linear model are considered in this paper.Firstly,the risks of the proposed estimators are derived and compared...The performances of preliminary test estimators for error variance based on W,LR and LM tests in a normal linear model are considered in this paper.Firstly,the risks of the proposed estimators are derived and compared by theoretical analysis and numerical calculation,respectively.The results show that their risks are related to the equality constraint error and the critical value of test.Moreover,the minimum value of the risks can be achieved when the critical value of test equals to one.Secondly,the superiority conditions of the proposed estimators are discussed.Finally,the results are illustrated by a simulation example.展开更多
In the past decade,significant progress has been made regarding inference under covariate-adaptive randomisation.We thank Prof.Shao for a timely review of the growing literature about the topic.The paper is focused on...In the past decade,significant progress has been made regarding inference under covariate-adaptive randomisation.We thank Prof.Shao for a timely review of the growing literature about the topic.The paper is focused on the most important and commonly used class of covariate-adaptive randomisation methods,i.e.,those balancing discrete covariates.The recent advances in robust inference are emphasised and discussed in detail.Several types of outcomes,such as continuous and time-to-event data,are covered.We here provide some additional recent results from the following five perspectives.展开更多
The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple management.Traditionally,2D imaging technologies were employed to del...The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple management.Traditionally,2D imaging technologies were employed to delineate the architectural traits of apple trees,but their accuracy was hampered by occlusion and perspective ambiguities.This study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization,utilizing point clouds collected by a terrestrial laser scanner(TLS).The pipeline consisted of four modules:(a)data preprocessing module,(b)tree instance segmentation module,(c)tree structure segmentation module,and(d)architectural trait extraction module.The developed pipeline was used to analyze 84 trees of two representative apple cultivars,characterizing architectural traits such as tree height,trunk diameter,branch count,branch diameter,and branch angle.Experimental results indicated that the established pipeline attained an R^(2)of 0.92 and 0.83,and a mean absolute error(MAE)of 6.1cm and 4.71mm for tree height and trunk diameter at the tree level,respectively.Additionally,at the branch level,it achieved an R^(2)of 0.77 and 0.69,and a MAE of 6.86 mm and 7.48°for branch diameter and angle,respectively.The accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding programs.Moreover,bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.展开更多
Life expectancy is increasing,leading to the continuous aging of the population in China.Enhancing the health status of the older population is crucial to achieving healthy aging.The primary objective of the PENG ZU S...Life expectancy is increasing,leading to the continuous aging of the population in China.Enhancing the health status of the older population is crucial to achieving healthy aging.The primary objective of the PENG ZU Study on Healthy Aging in China(PENG ZU Cohort)is to understand the natural progression of health status among the aging Chinese population.Specifically,the PENG ZU cohort aims to identify and validate multidimensional aging markers,uncover the underlying mechanisms of systemic aging and functional decline,and develop novel strategies and measures to delay functional decline and adverse health outcomes,while maintaining overall good health.The PENG ZU cohort consists of 26,000 individuals aged 25 to 89 years from seven major geographical regions in China.Diversified data and biospecimens are collected according to standardized procedures at baseline and follow-up visits.Baseline recruitment for the PENG ZU cohort was completed in October 2021.The extensive analysis of multidimensional health-related data and bioresources collected from the cohort is anticipated to develop methods for evaluating functional status and elucidating multilevel,cross-scale interactions and regulatory mechanisms of healthy aging.The findings from this study will enhance the understanding of health changes due to aging,facilitate efficient and effective interventions to maintain functional ability,and reduce the incidence and severity of age-related diseases,thereby further promoting healthy aging.展开更多
The purpose of this paper is two fold. First, we investigate estimation for varying coefficient partially linear models in which covariates in the nonparametric part are measured with errors. As there would be some sp...The purpose of this paper is two fold. First, we investigate estimation for varying coefficient partially linear models in which covariates in the nonparametric part are measured with errors. As there would be some spurious covariates in the linear part, a penalized profile least squares estimation is suggested with the assistance from smoothly clipped absolute deviation penalty. However, the estimator is often biased due to the existence of measurement errors, a bias correction is proposed such that the estimation consistency with the oracle property is proved. Second, based on the estimator, a test statistic is constructed to check a linear hypothesis of the parameters and its asymptotic properties are studied. We prove that the existence of measurement errors causes intractability of the limiting null distribution that requires a Monte Carlo approximation and the absence of the errors can lead to a chi-square limit. Furthermore, confidence regions of the parameter of interest can also be constructed. Simulation studies and a real data example are conducted to examine the performance of our estimators and test statistic.展开更多
We are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables.We suggest a profile least squares approach to perform partial dimension reduction for a gen...We are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables.We suggest a profile least squares approach to perform partial dimension reduction for a general class of semi-parametric models.The asymptotic properties of the resulting estimates for the central partial mean subspace and the mean function are provided.In addition,a Wald-type test is proposed to evaluate a linear hypothesis of the central partial mean subspace,and a generalized likelihood ratio test is constructed to check whether the nonparametric mean function has a specific parametric form.These tests can be used to evaluate whether there exist interactions between the covariates and the controlling variables,and if any,in what form.A Bayesian information criterion(BIC)-type criterion is applied to determine the structural dimension of the central partial mean subspace.Its consistency is also established.Numerical studies through simulations and real data examples are conducted to demonstrate the power and utility of the proposed semi-parametric approaches.展开更多
Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become in...Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become increasingly important in today’s clinical trials.The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study.Pocock and Simon’s procedure has been extended to the multi-arm case.However,the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades.In this paper,we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case,and establish the corresponding theory under widely satisfied conditions.The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization.Furthermore,these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariateadaptive randomization procedures.展开更多
High-dimensional data are frequently collected in a large variety of areas such as biomedical imaging,functional magnetic resonance imaging,tomography,tumor classifications,and finance.With recent explosion of scienti...High-dimensional data are frequently collected in a large variety of areas such as biomedical imaging,functional magnetic resonance imaging,tomography,tumor classifications,and finance.With recent explosion of scientific data of unprecedented size and complexity,feature ranking and screening are playing an increasingly important role in many scientific studies.展开更多
The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structu...The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space(RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.11873066 and U1731109)。
文摘Spectrum denoising is an important procedure for large-scale spectroscopical surveys. This work proposes a novel stellar spectrum denoising method based on deep Bayesian modeling. The construction of our model includes a prior distribution for each stellar subclass, a spectrum generator and a flow-based noise model. Our method takes into account the noise correlation structure, and it is not susceptible to strong sky emission lines and cosmic rays. Moreover, it is able to naturally handle spectra with missing flux values without ad-hoc imputation. The proposed method is evaluated on real stellar spectra from the Sloan Digital Sky Survey(SDSS) with a comprehensive list of common stellar subclasses and compared to the standard denoising auto-encoder. Our denoising method demonstrates a superior performance to the standard denoising auto-encoder, in respect of denoising quality and missing flux imputation. It may be potentially helpful in improving the accuracy of the classification and physical parameter measurement of stars when applying our method during data preprocessing.
基金supported by the National Natural Science Foundation of China under Grant Nos.11661003,11571073,11831008,11971235the Natural Science Foundation of Jiangxi Province under Grant Nos.20161BAB201033,20192BAB201006Science and Technology Project of Education Department of Jiangxi Province under Grant Nos.GJJ150582,GJJ160559
文摘The performances of preliminary test estimators for error variance based on W,LR and LM tests in a normal linear model are considered in this paper.Firstly,the risks of the proposed estimators are derived and compared by theoretical analysis and numerical calculation,respectively.The results show that their risks are related to the equality constraint error and the critical value of test.Moreover,the minimum value of the risks can be achieved when the critical value of test equals to one.Secondly,the superiority conditions of the proposed estimators are discussed.Finally,the results are illustrated by a simulation example.
基金partly supported by National Natural Science Foundation of China[GrantNumber 11801559],[Grant Number 12031005].
文摘In the past decade,significant progress has been made regarding inference under covariate-adaptive randomisation.We thank Prof.Shao for a timely review of the growing literature about the topic.The paper is focused on the most important and commonly used class of covariate-adaptive randomisation methods,i.e.,those balancing discrete covariates.The recent advances in robust inference are emphasised and discussed in detail.Several types of outcomes,such as continuous and time-to-event data,are covered.We here provide some additional recent results from the following five perspectives.
基金supported by the USDA NIFA Hatch project(accession no.1025032)USDA NIFA Specialty Crop Research Initiative(award no.2020-51181-32197)+4 种基金the McIntire-Stennis award(accession 1027551)from the United States Department of Agriculture Institute of Food and AgricultureCornell Institute of Digital Agriculture Research Innovation FundBeijing Municipal Natural Science Foundation(grant no.1232019)National Natural Science Foundation of China(grant no.12101606)Renmin University of China Research Fund Program for Young Scholars.
文摘The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple management.Traditionally,2D imaging technologies were employed to delineate the architectural traits of apple trees,but their accuracy was hampered by occlusion and perspective ambiguities.This study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization,utilizing point clouds collected by a terrestrial laser scanner(TLS).The pipeline consisted of four modules:(a)data preprocessing module,(b)tree instance segmentation module,(c)tree structure segmentation module,and(d)architectural trait extraction module.The developed pipeline was used to analyze 84 trees of two representative apple cultivars,characterizing architectural traits such as tree height,trunk diameter,branch count,branch diameter,and branch angle.Experimental results indicated that the established pipeline attained an R^(2)of 0.92 and 0.83,and a mean absolute error(MAE)of 6.1cm and 4.71mm for tree height and trunk diameter at the tree level,respectively.Additionally,at the branch level,it achieved an R^(2)of 0.77 and 0.69,and a MAE of 6.86 mm and 7.48°for branch diameter and angle,respectively.The accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding programs.Moreover,bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.
基金Supported by the National Key Research and Development Program of China(2018YFC2000300,2020YFC2002700).
文摘Life expectancy is increasing,leading to the continuous aging of the population in China.Enhancing the health status of the older population is crucial to achieving healthy aging.The primary objective of the PENG ZU Study on Healthy Aging in China(PENG ZU Cohort)is to understand the natural progression of health status among the aging Chinese population.Specifically,the PENG ZU cohort aims to identify and validate multidimensional aging markers,uncover the underlying mechanisms of systemic aging and functional decline,and develop novel strategies and measures to delay functional decline and adverse health outcomes,while maintaining overall good health.The PENG ZU cohort consists of 26,000 individuals aged 25 to 89 years from seven major geographical regions in China.Diversified data and biospecimens are collected according to standardized procedures at baseline and follow-up visits.Baseline recruitment for the PENG ZU cohort was completed in October 2021.The extensive analysis of multidimensional health-related data and bioresources collected from the cohort is anticipated to develop methods for evaluating functional status and elucidating multilevel,cross-scale interactions and regulatory mechanisms of healthy aging.The findings from this study will enhance the understanding of health changes due to aging,facilitate efficient and effective interventions to maintain functional ability,and reduce the incidence and severity of age-related diseases,thereby further promoting healthy aging.
基金supported by National Natural Science Foundation of China (Grant Nos. 11401006, 11671299 and 11671042)a grant from the University Grants Council of Hong Kong+1 种基金the China Postdoctoral Science Foundation (Grant No. 2017M611083)the National Statistical Science Research Program of China (Grant No. 2015LY55)
文摘The purpose of this paper is two fold. First, we investigate estimation for varying coefficient partially linear models in which covariates in the nonparametric part are measured with errors. As there would be some spurious covariates in the linear part, a penalized profile least squares estimation is suggested with the assistance from smoothly clipped absolute deviation penalty. However, the estimator is often biased due to the existence of measurement errors, a bias correction is proposed such that the estimation consistency with the oracle property is proved. Second, based on the estimator, a test statistic is constructed to check a linear hypothesis of the parameters and its asymptotic properties are studied. We prove that the existence of measurement errors causes intractability of the limiting null distribution that requires a Monte Carlo approximation and the absence of the errors can lead to a chi-square limit. Furthermore, confidence regions of the parameter of interest can also be constructed. Simulation studies and a real data example are conducted to examine the performance of our estimators and test statistic.
基金supported by Humanities and Social Science Foundation of Ministry of Education(Grant No.20YJC910003)Natural Science Foundation of Shanghai(Grant No.20ZR1423000)+1 种基金supported by Natural Science Foundation of Beijing(Grant No.Z19J0002)National Natural Science Foundation of China(Grant Nos.11731011 and 11931014)。
文摘We are concerned with partial dimension reduction for the conditional mean function in the presence of controlling variables.We suggest a profile least squares approach to perform partial dimension reduction for a general class of semi-parametric models.The asymptotic properties of the resulting estimates for the central partial mean subspace and the mean function are provided.In addition,a Wald-type test is proposed to evaluate a linear hypothesis of the central partial mean subspace,and a generalized likelihood ratio test is constructed to check whether the nonparametric mean function has a specific parametric form.These tests can be used to evaluate whether there exist interactions between the covariates and the controlling variables,and if any,in what form.A Bayesian information criterion(BIC)-type criterion is applied to determine the structural dimension of the central partial mean subspace.Its consistency is also established.Numerical studies through simulations and real data examples are conducted to demonstrate the power and utility of the proposed semi-parametric approaches.
基金supported by the National Key R&D Program of China (Grant No.2018YFC2000302)National Natural Science Foundation of China (Grant Nos.11731012,11731011 and 12031005)+1 种基金Ten Thousands Talents Plan of Zhejiang Province (Grant No.2018R52042)the Fundamental Research Funds for the Central Universities。
文摘Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become increasingly important in today’s clinical trials.The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study.Pocock and Simon’s procedure has been extended to the multi-arm case.However,the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades.In this paper,we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case,and establish the corresponding theory under widely satisfied conditions.The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization.Furthermore,these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariateadaptive randomization procedures.
文摘High-dimensional data are frequently collected in a large variety of areas such as biomedical imaging,functional magnetic resonance imaging,tomography,tumor classifications,and finance.With recent explosion of scientific data of unprecedented size and complexity,feature ranking and screening are playing an increasingly important role in many scientific studies.
基金supported by National Natural Science Foundation of China under Grant No.11771447。
文摘The estimation of high dimensional covariance matrices is an interesting and important research topic for many empirical time series problems such as asset allocation. To solve this dimension dilemma, a factor structure has often been taken into account. This paper proposes a dynamic factor structure whose factor loadings are generated in reproducing kernel Hilbert space(RKHS), to capture the dynamic feature of the covariance matrix. A simulation study is carried out to demonstrate its performance. Four different conditional variance models are considered for checking the robustness of our method and solving the conditional heteroscedasticity in the empirical study. By exploring the performance among eight introduced model candidates and the market baseline, the empirical study from 2001 to 2017 shows that portfolio allocation based on this dynamic factor structure can significantly reduce the variance, i.e., the risk, of portfolio and thus outperform the market baseline and the ones based on the traditional factor model.
