Semiparametric Error Research Articles

A calibration method for predicting and compensating the flexible positioning errors of a 7-DOF series compliant robot caused by end-effector’s gravity

Due to low stiffness of compliant series robot body, the influence of joint flexible deformation caused by the gravity load of the end-effector on the absolute positioning accuracy cannot be ignored. In order to improve the flexible positioning accuracy of the robot end-effector, a calibration method for predicting and compensating the flexible positioning error of the 7-DOF series compliant robot due to the gravity load of the end-effector is proposed. The method identifies and predicts the pose configuration with similar flexible positioning error through the semi-parametric error model based on joint stiffness identification and of BP neural network. The feedforward method is used to compensate the positioning error. In this study, a 7-DOF series compliant robot is selected as the research object. Based on the principle of error similarity, the measurement points and the corresponding pose configurations are selected as the training data for the parameter identification of the semi-parametric error model, and the flexible positioning errors on the continuous trajectory of the end-effector are compensated according to the predicted values of the model. When the mass of the end-effector is 5 kg, by this method, the maximum value, mean value and mean square error of flexible positioning error on multiple straight-line trajectories on the plane are increased from 8.4779, 5.6039, and 1.8496 mm to 0.9720, 0.3079, and 0.2303 mm, respectively. It means the positioning accuracy are increased by 88.53%, 94.50%, and 87.55%, respectively, by which could observe that the precision of this robot’s end-effector was significantly improved.

Work-limiting disabilities and earnings volatility

This paper examines how the onset of a work-limiting disability influences male earnings volatility as measured by the variance of transitory earnings. Using data from the 1970 to 2013 survey waves of the Panel Study of Income Dynamics and a semi-parametric error components model, we decompose annual earnings into its permanent and transitory components. Results show that the variance of transitory earnings of disabled and non-disabled men is similar before onset of a work-limiting health condition. Upon onset, earnings volatility increases significantly, and does not return to pre-onset levels until at least five years after the last reported disability. Additional results show that earnings volatility has increased since 1970 for both disabled and non-disabled men. However, the trend in volatility is steeper for those who suffer from a disability. Despite this steeper trend, disability's contribution to the total variance of earnings has remained constant over time at roughly 15%.

Simultaneous WiFi Ranging Compensation and Localization for Indoor NLoS Environments

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively.

Combined effects of some chemicals on beta–galactosidase activity using a new semiparametric error in variables model

Combined effects of some chemicals on beta–galactosidase activity using a new semiparametric error in variables model

Comparing parametric and semiparametric error correction models for estimation of long run equilibrium between exports and imports

This paper introduces the semiparametric error correction model for estimation of export-import relationship as an alternative to the least squares approach. The intent is to demonstrate how semiparametric error correction model can be used to estimate the relationship between Ghana’s export and import within the context of a generalized additive modelling (GAM) framework. The semiparametric results are compared to common parametric specification using the ordinary least squares regression. The results from the semiparametric and parametric error correction models (ECM) indicate that the error correction term and import variable are significant determinants of Ghana’s exports. On the basis of Akaike Information Criteria and Generalized Cross-Validation (GCV) scores, it is found that the semiparametric error correction model provides a better fit than the widely used parametric error correction model for modeling Ghana’s export-import relationship. The results of the analysis of variance provide further evidence of nonlinearity in Ghana’s export and import relationship. In effect, this paper demonstrates the usefulness of semiparametric error correction model in the estimation of export – import relationship.
 JEL code: C14, C18, C22, F10, F14

Rank-Based Tests of the Cointegrating Rank in Semiparametric Error Correction Models

This paper introduces rank-based tests for the cointegrating rank in an Error CorrectionModel with i.i.d. elliptical innovations. The tests are asymptotically distribution-free,and their validity does not depend on the actual distribution of the innovations. Thisresult holds despite the fact that, depending on the alternatives considered, the model exhibitsa non-standard Locally Asymptotically Brownian Functional (LABF) and LocallyAsymptotically Mixed Normal (LAMN) local structure—a structure which we completelycharacterize. Our tests, which have the general form of Lagrange multiplier tests, dependon a reference density that can freely be chosen, and thus is not restricted to be Gaussianas in traditional quasi-likelihood procedures. Moreover, appropriate choices of the referencedensity are achieving the semiparametric efficiency bounds. Simulations show thatour asymptotic analysis provides an accurate approximation to finite-sample behavior.Our results are based on an extension, of independent interest, of two abstract resultson the convergence of statistical experiments and the asymptotic linearity of statistics tothe context of, possibly non-stationary, time series (This abstract was borrowed from another version of this item.)

Effects of Introducing Five-Day Work Week in Korean Labor Market: A Semiparametric Vector Error Correction Approach

This paper uses the semiparametric error correction model (Li and Wooldridge, 2002) and investigates the dynamics of wage, employment and labor efficiency after introducing five-day work week to the Korean labor market. Efficient working hour function is obtained by nonparametric method (polynomial approximation) and the dynamics are investigated using impulse response analysis. Average derivative estimator is used to derive the impulse response function of the semiparametric error correction model. The results show that reducing working hours would not create new employment and thus it, as a labor policy, could not be a solution for unemployment. On the other hand, cutting working hours boosts labor efficiency so sharply that the efficient labor input increases. It is also shown that the Korean labor market is flexible enough to adjust the shock within a year.

Is the term structure nonlinear? A semiparametric investigation

A semiparametric error correction model (ECM) is estimated using US term structure data. We use 5 and 10 year interest rates to predict short-term (1 month to 12 month) interest rates. It is found that the semiparametric ECM model predicts better than the popular linear ECM. These results provide further evidence of nonlinearity in the term structure.