Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression … (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination …

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression … (SVR) has been successfully used to solve nonlinear regression and times series problems. However, parameters determination …

new algorithm which is based on the regression later approach. Under some regularity assumptions, the solution of a … regression later algorithm based on the least squares Monte Carlo method and the previous connection between the quasi … backward Euler-Maruyama scheme. Subsequently, we will describe the regression later algorithm, in further detail the algorithm …

Logistic regression is widely used in complex data analysis. When predictors are at individual level and the response … at aggregate level, logistic regression can be estimated using the Maximum Likelihood Estimation (MLE) method with the …-maximization (EM) algorithm to avoid the direct maximization of the complicated likelihood function. Simulation studies have been …