다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

On robust estimation of the location parameter

비만아 부모의 돌봄 경험: q 방법론

Matrix Differential Calculus with Applications in Statistics and Econometrics

Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T We give bias corrections for averages over the fixed effects, as well as model parameters We find large bias reductions from using these approaches in examples We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications We show that if T grows at the same rate as n the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife We show T growing faster than n1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife

Jackknife and analytical bias reduction for nonlinear panel models

Filtering of ground points is a key step for most applications of airborne LiDAR point clouds. Although many filtering algorithms have been proposed in recent years, most of them suffer from parameter setting or thresholds fine-tuning. This is most often time-consuming and reduces the degree of automation of the applied algorithm. To overcome such problems, this paper proposes a threshold-free filtering algorithm based on expectation–maximization (EM). The filter is developed based on the assumption that point clouds are seen as a mixture of Gaussian models. Thus, the separation of ground points and non-ground points from point clouds is partitioning of the point clouds by a mixed Gaussian model that is used for screening ground points. EM is applied to realize the separation, which calculates the maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or non-ground are computed. Noticeably, point clouds are labeled as the component with a larger likelihood. The proposed method has been tested using the standard filtering datasets provided by the ISPRS. Experimental results showed that the proposed method performed the best in comparison with the classic progressive triangulated irregular network densification (PTD) and segment-based PTD methods in terms of omission error. The average omission error of the proposed method was 52.81% and 16.78% lower than the classic PTD method and the segment-based PTD method, respectively. Moreover, the proposed method was able to reduce its average total error by 31.95% compared to the classic PTD method.

Automatic DTM extraction from airborne LiDAR based on expectation-maximization

Source parameters and triggering links of the earthquake sequence in central Italy from 2009 to 2016 analyzed with GPS and InSAR data

The errors-in-variables (EIV) model is a nonlinear model, the parameters of which can be solved by singular value decomposition (SVD) method or the general iterative algorithm. The existing formulae for covariance matrix of total least squares (TLS) parameter estimates don't fully consider the randomness of quantities in iterative algorithm and the biases of parameter estimates and residuals. In order to reflect more reasonable precision information for TLS adjustment, the derivative-free unscented transformation with scaled symmetric sampling strategy, i.e. scaled unscented transformation (SUT), is introduced and implemented. In this contribution, we firstly discuss the existing various solutions of TLS adjustment and covariance matrices of TLS parameter estimates and derive the general first-order approximate cofactor matrices of random quantities in TLS adjustment. Secondly, based on the combination of TLS iterative algorithm and calculation process of SUT, we design the two SUT algorithms to calculate the biases and the second-order approximate covariance matrices. Finally, the straight line fitting model and plane coordinate transformation model are used to demonstrate that applying SUT for precision estimation of TLS adjustment is feasible and effective.

Unscented transformation with scaled symmetric sampling strategy for precision estimation of total least squares

Scaled Unscented Transformation of Nonlinear Error Propagation: Accuracy, Sensitivity, and Applications

An iterative algorithm for variance component estimation based on partial errors-invariables (PEIV) model is proposed. Correction of observation vector and random elements of the coefficient matrix is taken as one kind of posterior information. Variance components in the observation vector and the random elements of the coefficient matrix are estimated according to Helmert estimation method. During the estimating process, the correction factors are used to modify the initial weight matrix, so as to make it more accurate. At the same time, a method for determining correction factors is given. Through examples of linear fitting and numerical simulation experiment of coordinate transformation, the practical effect of this algorithm is verified.

Leyang Wang

Papers

Automatic DTM extraction from airborne LiDAR based on expectation-maximization

Source parameters and triggering links of the earthquake sequence in central Italy from 2009 to 2016 analyzed with GPS and InSAR data

Unscented transformation with scaled symmetric sampling strategy for precision estimation of total least squares

Scaled Unscented Transformation of Nonlinear Error Propagation: Accuracy, Sensitivity, and Applications

Variance component estimation for partial errors-in-variables models