The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by r...The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by response rate. Each of them has merits as well as weakness. In order to incorporate the merits of the methods mentioned above, we put forward calibration estimation, which suggests adjusting the preliminary weights by auxiliary information at the stage of estimating. Marke the best of the relations between the independent variables and the dependent variable, use appropriate estimation method, and you’ll get a good estimator for the sum of the target variable.展开更多
In this paper, a novel calibration integral equation is derived for resolving double-sided, two-probe inverse heat conduction problem of surface heat flux estimation. In contrast to the conventional inverse heat condu...In this paper, a novel calibration integral equation is derived for resolving double-sided, two-probe inverse heat conduction problem of surface heat flux estimation. In contrast to the conventional inverse heat conduction techniques, this calibration approach does not require explicit input of the probe locations, thermophysical properties of the host material and temperature sensor parameters related to thermal contact resistance, sensor capacitance and conductive lead losses. All those parameters and properties are inherently contained in the calibration framework in terms of Volterra integral equation of the first kind. The Laplace transform technique is applied and the frequency domain manipulations of the heat equation are performed for deriving the calibration integral equation. Due to the ill-posed nature, regularization is required for the inverse heat conduction problem, a future-time method or singular value decomposition (SVD) can be used for stabilizing the ill-posed Volterra integral equation of the first kind.展开更多
文摘The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by response rate. Each of them has merits as well as weakness. In order to incorporate the merits of the methods mentioned above, we put forward calibration estimation, which suggests adjusting the preliminary weights by auxiliary information at the stage of estimating. Marke the best of the relations between the independent variables and the dependent variable, use appropriate estimation method, and you’ll get a good estimator for the sum of the target variable.
文摘In this paper, a novel calibration integral equation is derived for resolving double-sided, two-probe inverse heat conduction problem of surface heat flux estimation. In contrast to the conventional inverse heat conduction techniques, this calibration approach does not require explicit input of the probe locations, thermophysical properties of the host material and temperature sensor parameters related to thermal contact resistance, sensor capacitance and conductive lead losses. All those parameters and properties are inherently contained in the calibration framework in terms of Volterra integral equation of the first kind. The Laplace transform technique is applied and the frequency domain manipulations of the heat equation are performed for deriving the calibration integral equation. Due to the ill-posed nature, regularization is required for the inverse heat conduction problem, a future-time method or singular value decomposition (SVD) can be used for stabilizing the ill-posed Volterra integral equation of the first kind.
