This work studies a proportional hazards model for survival data with "long-term survivors",in which covariates are subject to linear measurement error.It is well known that the naive estimators from both pa...This work studies a proportional hazards model for survival data with "long-term survivors",in which covariates are subject to linear measurement error.It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model.For measurement error models,methods of unbiased estimating function and corrected likelihood have been proposed in the literature.In this paper,we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors.The asymptotic properties of the estimators are established.Simulation results illustrate that the proposed approaches provide useful tools for the models considered.展开更多
This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerometer ...This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerometer output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observerbased control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compensate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the traditional method, the proposed method can improve the attitude steady accuracy effectively.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
基金supported by the National Nature Science Foundation of China under Grant No.10871084Macquarie University Safety Net grant
文摘This work studies a proportional hazards model for survival data with "long-term survivors",in which covariates are subject to linear measurement error.It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model.For measurement error models,methods of unbiased estimating function and corrected likelihood have been proposed in the literature.In this paper,we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors.The asymptotic properties of the estimators are established.Simulation results illustrate that the proposed approaches provide useful tools for the models considered.
基金supported by the National Natural Science Foundation of China(Grant Nos.61174121,61333005 and 61121003)the Ph.D Programs Foundations of the Ministry of Education China
文摘This paper deals with the problem of accelerometer error estimation and compensation for a three-axis gyro-stabilized camera mount. In a dynamic environment, the aircraft motion acceleration affects the accelerometer output and causes a degradation of attitude steady accuracy. In order to improve control accuracy, this paper proposes a proportional multiple-integral observerbased control strategy to estimate and compensate the accelerometer error. The basic idea of this paper is to approximate the error property by using a q-order polynomial function and extend the error and its derivatives as augmented states. Then a proportional multiple-integral observer is developed to estimate the error, with which the relationship between the error and the imbalance torque is formulated. The estimated value is compared to an angle threshold, the result of which is used to compensate the accelerometer output. Through static and vehicle-mounted experiments, it is demonstrated that compared with the traditional method, the proposed method can improve the attitude steady accuracy effectively.
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.
