Use of penalty functions in calibration estimators has severally been considered by this author. A calibration problem is transformed to an unconstrained optimization problem by constructing a penalty function. To guarantee convergence in the minimization of the penalty function by the Newton method, the order of the penalty function is usually restricted to 2. In this paper, we consider use of more flexible higher order penalty functions by applying the variable metric method. We report on the results of the resulting population total estimator for a cubic penalty function. Keywords: variable metric method, calibration, penalty function, model calibration, unconstrained optimization
Compared with ordinary regression models, the computational cost for estimating parame-ters in gener...
Calibration is a technique of adjusting sample weights routinely used in sample surveys. In this cha...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...
Use of nonparametric model calibration estimators for population total and mean has been considered ...
AbstractThis paper describes a class of variable penalty methods for solving the general nonlinear p...
Estimation of finite population total using calibration has been considered by several authors. A di...
AbstractRecent efforts in differentiable non-linear programming have been focused on interior point ...
Recent efforts in differentiable non-linear programming have been focused on interior point methods,...
Various forms of penalty functions have been developed for regularized estimation and variable selec...
The penalty function method is a mathematical approach generally used in non linear programming and ...
We study model selection strategies based on penalized empirical loss minimization. We point out a...
In the context of the high-dimensional Gaussian linear regression for ordered variables, we study th...
When we have prior information of approximate and subjective nature other than those contained in th...
Various forms of penalty functions have been developed for regularized estimation. The tuning parame...
We propose variable selection procedures based on penalized score functions derived for linear measu...
Compared with ordinary regression models, the computational cost for estimating parame-ters in gener...
Calibration is a technique of adjusting sample weights routinely used in sample surveys. In this cha...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...
Use of nonparametric model calibration estimators for population total and mean has been considered ...
AbstractThis paper describes a class of variable penalty methods for solving the general nonlinear p...
Estimation of finite population total using calibration has been considered by several authors. A di...
AbstractRecent efforts in differentiable non-linear programming have been focused on interior point ...
Recent efforts in differentiable non-linear programming have been focused on interior point methods,...
Various forms of penalty functions have been developed for regularized estimation and variable selec...
The penalty function method is a mathematical approach generally used in non linear programming and ...
We study model selection strategies based on penalized empirical loss minimization. We point out a...
In the context of the high-dimensional Gaussian linear regression for ordered variables, we study th...
When we have prior information of approximate and subjective nature other than those contained in th...
Various forms of penalty functions have been developed for regularized estimation. The tuning parame...
We propose variable selection procedures based on penalized score functions derived for linear measu...
Compared with ordinary regression models, the computational cost for estimating parame-ters in gener...
Calibration is a technique of adjusting sample weights routinely used in sample surveys. In this cha...
Optimization problems arise in science, engineering, economy, etc. and we need to find the best sol...