Fractional Gradient Descent-Based Auxiliary Model Algorithm for FIR Models with Missing Data

Parametric fractional imputation for mixed models with nonignorable missing data

Inference in the presence of non-ignorable missing data is a widely encountered and difficult problem in statistics. Imputation is often used to facilitate parameter estimation, which allows one to use the complete sample estimators on the imputed data set. We develop a parametric fractional imputation (PFI) method proposed by Kim (2011), which simplifies the computation associated with the EM ...

Auxiliary Variables Predicting Missing Data

Improved Stochastic gradient descent algorithm for SVM

In order to improve the efficiency and classification ability of Support vector machines (SVM) based on stochastic gradient descent algorithm, three algorithms of improved stochastic gradient descent (SGD) are used to solve support vector machine, which are Momentum, Nesterov accelerated gradient (NAG), RMSprop. The experimental results show that the algorithm based on RMSprop for solving the l...

Multiple-Gradient Descent Algorithm (MGDA)

In a previous report [3], a methodology for the numerical treatment of a two-objective optimization problem, possibly subject to equality constraints, was proposed. The method was devised to be adapted to cases where an initial design-point is known and such that one of the two disciplines, considered to be preponderant, or fragile, and said to be the primary discipline, achieves a local or glo...

Mutiple-gradient Descent Algorithm for Multiobjective Optimization

The steepest-descent method is a well-known and effective single-objective descent algorithm when the gradient of the objective function is known. Here, we propose a particular generalization of this method to multi-objective optimization by considering the concurrent minimization of n smooth criteria {J i } (i = 1,. .. , n). The novel algorithm is based on the following observation: consider a...