Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is q...Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.展开更多
Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel mo...Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator＇s arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable （SRIV） identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the.real machine motion control exoeriments.展开更多
In this paper,the estimation for a class of generalized varying coefficient models with error-prone covariates is considered.By combining basis function approximations with some auxiliary variables,an instrumental var...In this paper,the estimation for a class of generalized varying coefficient models with error-prone covariates is considered.By combining basis function approximations with some auxiliary variables,an instrumental variable type estimation procedure is proposed.The asymptotic results of the estimator,such as the consistency and the weak convergence rate,are obtained.The proposed procedure can attenuate the effect of measurement errors and have proved workable for finite samples.展开更多
Early discharge policy, common in the developed countries, refers to the reduction of hospital length of stay as a way of reducing the cost of care. The effect of the policy on quality of care has received a lot of at...Early discharge policy, common in the developed countries, refers to the reduction of hospital length of stay as a way of reducing the cost of care. The effect of the policy on quality of care has received a lot of attention in the literature. Some of the earlier papers have ignored the endogeneity of length of stay in the readmission equation, an approach that could lead to inconsistent estimation. This study develops a statistical technique for the consistent estimation of the effect of the early discharge policy. An instrument that can be used extensively across different diagnostic groups is provided, hence solving the difficult problem of finding an instrument for length of stay. The exogeneity test in Gorgger (1990), the test for weak instruments in Staiger and Stock (1997) as well as the Hensen (1982) for over identification confirmed respectively that length of stay is endogenous the instrument is strong and the valid.展开更多
Economic modeling that yields practical value must cater for effects caused by exogenous variables. AutoRegressive eXogenous approach (ARX) has been widely used in regional economic studies. Instrumental Variable Meth...Economic modeling that yields practical value must cater for effects caused by exogenous variables. AutoRegressive eXogenous approach (ARX) has been widely used in regional economic studies. Instrumental Variable Method is regarded as a preferential method to parametric estimation in ARX modeling. However, traditional instrumental variable methods can only handle single variable which has limited its capability. This paper presents an extended instrumental variable method (EIVM) which is based on multiple variables. This provides the capability of taking into account of exogenous variables and reflects better the economic activities. A case study is conducted, which illustrates the application of the EIVM in modeling Northeastern economy in China.展开更多
随着手机镜头成像距离范围的拓展,成像质量的测试范围也需要同步拓展。传统测试方法需频繁移动图卡,效率和精度受限。为了满足高精度调制传递函数(Modulation Transfer Function,MTF)测量的需求,设计并实现了一款可变模拟物距的平行光...随着手机镜头成像距离范围的拓展,成像质量的测试范围也需要同步拓展。传统测试方法需频繁移动图卡,效率和精度受限。为了满足高精度调制传递函数(Modulation Transfer Function,MTF)测量的需求,设计并实现了一款可变模拟物距的平行光管系统,可实现从150?mm至无穷远的物距模拟,具备16?mm的出瞳直径和75?mm的工作距离,适用于多数光学测试场景。使用ZEMAX对系统进行了多波长、多模拟物距条件下的优化,结果表明该光学系统在各项指标上均接近衍射极限。对系统的公差分析表明其具备良好的可加工性。镜头加工完成后,采用Trioptics公司的ImageMaster Universal进行MTF测试,并通过分辨率测试板对系统分辨率进行评估。实测结果表明,系统在各模拟物距下均能保持良好的成像质量,验证了该设计的工程应用价值。展开更多
Growing evidence suggests that abnormal lipid metabolism occurs in amyotrophic lateral sclerosis,even in the presymptomatic stage,implying an etiologic link.However,the genetic mechanism underlying altered lipid level...Growing evidence suggests that abnormal lipid metabolism occurs in amyotrophic lateral sclerosis,even in the presymptomatic stage,implying an etiologic link.However,the genetic mechanism underlying altered lipid levels in amyotrophic lateral sclerosis remains elusive.Therefore,in this study,we performed genetic correlation analysis,a cross-trait meta-analysis,tissue-specific enrichment analysis,and bidirectional two-sample Mendelian randomization analysis of European population to explore whether there is a genetic and causal relationship between lipids and amyotrophic lateral sclerosis.The effect of lipid-lowering drugs on amyotrophic lateral sclerosis was also evaluated using a drug target Mendelian randomization approach.The results showed a positive genetic correlation between amyotrophic lateral sclerosis and both high-density lipoprotein cholesterol and apolipoprotein A1 and identified 71 independent shared loci between amyotrophic lateral sclerosis and high-density lipoprotein cholesterol,as well as 55 independent shared loci between amyotrophic lateral sclerosis and apolipoprotein A1.These shared loci were enriched in the lipid metabolic pathway and the alcohol metabolic pathway.Further Mendelian randomization analysis targeting lipid-lowering drugs showed that single nucleotide polymorphisms within the ACLY and PCSK9 genes had a protective effect against amyotrophic lateral sclerosis risk by decreasing low-density lipoprotein cholesterol.The combination of ACLY and PCSK9 inhibitors has a greater protective effect on amyotrophic lateral sclerosis risk than that of PCSK9 inhibitors alone.In summary,there is a common genetic structure between lipids and amyotrophic lateral sclerosis.Mendelian randomization analysis supports an association between elevated blood lipids and the risk of developing amyotrophic lateral sclerosis,and the use of ACLY or PCSK9 inhibitors may improve disease prognosis.展开更多
Conditional Local Risk Ratio(CLRR)is a widely used metric for assessing heterogeneous treatment effects of binary outcomes in randomized clinical trials involving noncompliance.Existing methods,such as moment-based an...Conditional Local Risk Ratio(CLRR)is a widely used metric for assessing heterogeneous treatment effects of binary outcomes in randomized clinical trials involving noncompliance.Existing methods,such as moment-based and likelihood-based approaches,often overlook the inherent mixture structure in data,necessitate stringent parametric assumptions,or yield estimates with implausible values.In this paper,we introduce a novel semiparametric likelihood-based(SPL)method for estimating CLRR.Our method requires only three parametric model assumptions,significantly fewer than the six models needed by existing likelihood-based methods,thereby reducing model complexity and enhancing robustness.This simplicity also results in fewer unknown parameters,further boosting computational efficiency.Unlike moment-based methods,our SPL method fully exploits the mixture structure of the observed data and the principal strata framework.Additionally,our method ensures that the final CLRR estimate always fall within a valid range.We establish the asymptotic normality of our estimator and demonstrate its superiority over existing methods through numerical simulations.We further apply our method to analyze the Oregon Health Insurance Experiment dataset,providing valuable insights into the heterogeneous effects of Medicaid on both physical and mental health.展开更多
Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to st...Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to strengthen causal inferences.One key assumption critical to the validity of the IV method is the exclusion assumption,which requires instruments to be correlated with the outcome variable only through endogenous predictors.The chi-square test of model fit is widely used as a diagnostic test for this assumption.Previous simulation studies assessed the power of this diagnostic test only in situations with strong violations of the exclusion assumption.However,low to moderate levels of assumption violation are not uncommon in reality,especially when the exclusion assumption is violated indirectly.In this study,we showed through Monte Carlo simulations that the chi-square model fit test suffered from a severe lack of power(<30%)to detect violations of the exclusion assumption when the level of violation was of typical size,and the IV causal inferences were severely inaccurate and misleading in this case.We thus advise using the IV method with caution unless there is a chance for thorough assumption diagnostics,like in meta-analyses or experiments.展开更多
Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to sele...Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.展开更多
Weconsider a model identification problem in which an outcome variable contains nonignorable missing values.Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoret...Weconsider a model identification problem in which an outcome variable contains nonignorable missing values.Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoretically reasonable properties such as consistency and asymptotic normality.Recently,instrumental or shadow variables,combined with the completeness condition in the outcome model,have been highlighted to make a model identifiable.In this paper,we elucidate the relationship between the completeness condition and model identifiability when the instrumental variable is categorical.We first show that when both the outcome and instrumental variables are categorical,the two conditions are equivalent.However,when one of the outcome and instrumental variables is continuous,the completeness condition may not necessarily hold,even for simple models.Consequently,we provide a sufficient condition that guarantees the identifiability of models exhibiting a monotone-likelihood property,a condition particularly useful in instances where establishing the completeness condition poses significant challenges.Using observed data,we demonstrate that the proposed conditions are easy to check for many practical models and outline their usefulness in numerical experiments and real data analysis.展开更多
基金supported by the National Key R&D Program of China under Grant No.2022YFA1003701the Open Research Fund of Yunnan Key Laboratory of Statistical Modeling and Data Analysis,Yunnan University under Grant No.SMDAYB2023004。
文摘Quantile regression(QR)has become an important tool to measure dependence of response variable's quantiles on a number of predictors for heterogeneous data,especially heavy-tailed data and outliers.However,it is quite challenging to make statistical inference on distributed high-dimensional QR with missing data due to the distributed nature,sparsity and missingness of data and nondifferentiable quantile loss function.To overcome the challenge,this paper develops a communicationefficient method to select variables and estimate parameters by utilizing a smooth function to approximate the non-differentiable quantile loss function and incorporating the idea of the inverse probability weighting and the penalty function.The proposed approach has three merits.First,it is both computationally and communicationally efficient because only the first-and second-order information of the approximate objective function are communicated at each iteration.Second,the proposed estimators possess the oracle property after a limited number of iterations without constraint on the number of machines.Third,the proposed method simultaneously selects variables and estimates parameters within a distributed framework,ensuring robustness to the specified response probability or propensity score function of the missing data mechanism.Simulation studies and a real example are used to illustrate the effectiveness of the proposed methodologies.
文摘Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator＇s arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable （SRIV） identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the.real machine motion control exoeriments.
基金Supported by the National Natural Science Foundation of China(11101119)the Natural Science Foundation of Guangxi(2010GXNSFB013051)the Philosophy and Social Sciences Foundation of Guangxi(11FTJ002)
文摘In this paper,the estimation for a class of generalized varying coefficient models with error-prone covariates is considered.By combining basis function approximations with some auxiliary variables,an instrumental variable type estimation procedure is proposed.The asymptotic results of the estimator,such as the consistency and the weak convergence rate,are obtained.The proposed procedure can attenuate the effect of measurement errors and have proved workable for finite samples.
文摘Early discharge policy, common in the developed countries, refers to the reduction of hospital length of stay as a way of reducing the cost of care. The effect of the policy on quality of care has received a lot of attention in the literature. Some of the earlier papers have ignored the endogeneity of length of stay in the readmission equation, an approach that could lead to inconsistent estimation. This study develops a statistical technique for the consistent estimation of the effect of the early discharge policy. An instrument that can be used extensively across different diagnostic groups is provided, hence solving the difficult problem of finding an instrument for length of stay. The exogeneity test in Gorgger (1990), the test for weak instruments in Staiger and Stock (1997) as well as the Hensen (1982) for over identification confirmed respectively that length of stay is endogenous the instrument is strong and the valid.
文摘Economic modeling that yields practical value must cater for effects caused by exogenous variables. AutoRegressive eXogenous approach (ARX) has been widely used in regional economic studies. Instrumental Variable Method is regarded as a preferential method to parametric estimation in ARX modeling. However, traditional instrumental variable methods can only handle single variable which has limited its capability. This paper presents an extended instrumental variable method (EIVM) which is based on multiple variables. This provides the capability of taking into account of exogenous variables and reflects better the economic activities. A case study is conducted, which illustrates the application of the EIVM in modeling Northeastern economy in China.
文摘随着手机镜头成像距离范围的拓展,成像质量的测试范围也需要同步拓展。传统测试方法需频繁移动图卡,效率和精度受限。为了满足高精度调制传递函数(Modulation Transfer Function,MTF)测量的需求,设计并实现了一款可变模拟物距的平行光管系统,可实现从150?mm至无穷远的物距模拟,具备16?mm的出瞳直径和75?mm的工作距离,适用于多数光学测试场景。使用ZEMAX对系统进行了多波长、多模拟物距条件下的优化,结果表明该光学系统在各项指标上均接近衍射极限。对系统的公差分析表明其具备良好的可加工性。镜头加工完成后,采用Trioptics公司的ImageMaster Universal进行MTF测试,并通过分辨率测试板对系统分辨率进行评估。实测结果表明,系统在各模拟物距下均能保持良好的成像质量,验证了该设计的工程应用价值。
基金funded by the National Natural Science Foundation of China,Nos.82401670(to KX),81873784(to DF),and 82071426(to DF)the Clinical Cohort Construction Program of Peking University Third Hospital,No.BYSYDL2019002(to DF)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation,No.GZC20230152(to KX).
文摘Growing evidence suggests that abnormal lipid metabolism occurs in amyotrophic lateral sclerosis,even in the presymptomatic stage,implying an etiologic link.However,the genetic mechanism underlying altered lipid levels in amyotrophic lateral sclerosis remains elusive.Therefore,in this study,we performed genetic correlation analysis,a cross-trait meta-analysis,tissue-specific enrichment analysis,and bidirectional two-sample Mendelian randomization analysis of European population to explore whether there is a genetic and causal relationship between lipids and amyotrophic lateral sclerosis.The effect of lipid-lowering drugs on amyotrophic lateral sclerosis was also evaluated using a drug target Mendelian randomization approach.The results showed a positive genetic correlation between amyotrophic lateral sclerosis and both high-density lipoprotein cholesterol and apolipoprotein A1 and identified 71 independent shared loci between amyotrophic lateral sclerosis and high-density lipoprotein cholesterol,as well as 55 independent shared loci between amyotrophic lateral sclerosis and apolipoprotein A1.These shared loci were enriched in the lipid metabolic pathway and the alcohol metabolic pathway.Further Mendelian randomization analysis targeting lipid-lowering drugs showed that single nucleotide polymorphisms within the ACLY and PCSK9 genes had a protective effect against amyotrophic lateral sclerosis risk by decreasing low-density lipoprotein cholesterol.The combination of ACLY and PCSK9 inhibitors has a greater protective effect on amyotrophic lateral sclerosis risk than that of PCSK9 inhibitors alone.In summary,there is a common genetic structure between lipids and amyotrophic lateral sclerosis.Mendelian randomization analysis supports an association between elevated blood lipids and the risk of developing amyotrophic lateral sclerosis,and the use of ACLY or PCSK9 inhibitors may improve disease prognosis.
基金supported by the National Natural Science Foundation of China[Grant numbers 12371293,32030063,12171157]。
文摘Conditional Local Risk Ratio(CLRR)is a widely used metric for assessing heterogeneous treatment effects of binary outcomes in randomized clinical trials involving noncompliance.Existing methods,such as moment-based and likelihood-based approaches,often overlook the inherent mixture structure in data,necessitate stringent parametric assumptions,or yield estimates with implausible values.In this paper,we introduce a novel semiparametric likelihood-based(SPL)method for estimating CLRR.Our method requires only three parametric model assumptions,significantly fewer than the six models needed by existing likelihood-based methods,thereby reducing model complexity and enhancing robustness.This simplicity also results in fewer unknown parameters,further boosting computational efficiency.Unlike moment-based methods,our SPL method fully exploits the mixture structure of the observed data and the principal strata framework.Additionally,our method ensures that the final CLRR estimate always fall within a valid range.We establish the asymptotic normality of our estimator and demonstrate its superiority over existing methods through numerical simulations.We further apply our method to analyze the Oregon Health Insurance Experiment dataset,providing valuable insights into the heterogeneous effects of Medicaid on both physical and mental health.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011986)National Natural Science Foundation of China(Grant No.31700986).
文摘Regression estimates are biased when potential confounders are omitted or when there are other similar risks to validity.The instrumental variable(IV)method can be used instead to obtain less biased estimates or to strengthen causal inferences.One key assumption critical to the validity of the IV method is the exclusion assumption,which requires instruments to be correlated with the outcome variable only through endogenous predictors.The chi-square test of model fit is widely used as a diagnostic test for this assumption.Previous simulation studies assessed the power of this diagnostic test only in situations with strong violations of the exclusion assumption.However,low to moderate levels of assumption violation are not uncommon in reality,especially when the exclusion assumption is violated indirectly.In this study,we showed through Monte Carlo simulations that the chi-square model fit test suffered from a severe lack of power(<30%)to detect violations of the exclusion assumption when the level of violation was of typical size,and the IV causal inferences were severely inaccurate and misleading in this case.We thus advise using the IV method with caution unless there is a chance for thorough assumption diagnostics,like in meta-analyses or experiments.
文摘Multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple testing.It is aiming to select the important variables among many variables.Performing variable selection in high-dimensional linear models with measurement errors is challenging.Both the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely biases.We consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection method.By constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables models.An extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.
基金supported by MEXT Project for Seismology toward Research Innovation with Data of Earthquake(STAR-E)[Grant Number JPJ010217].
文摘Weconsider a model identification problem in which an outcome variable contains nonignorable missing values.Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoretically reasonable properties such as consistency and asymptotic normality.Recently,instrumental or shadow variables,combined with the completeness condition in the outcome model,have been highlighted to make a model identifiable.In this paper,we elucidate the relationship between the completeness condition and model identifiability when the instrumental variable is categorical.We first show that when both the outcome and instrumental variables are categorical,the two conditions are equivalent.However,when one of the outcome and instrumental variables is continuous,the completeness condition may not necessarily hold,even for simple models.Consequently,we provide a sufficient condition that guarantees the identifiability of models exhibiting a monotone-likelihood property,a condition particularly useful in instances where establishing the completeness condition poses significant challenges.Using observed data,we demonstrate that the proposed conditions are easy to check for many practical models and outline their usefulness in numerical experiments and real data analysis.
