Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
JSTOR Daily

Linear regression MDP scheme for discrete backward stochastic differential equations under general conditions

Abstract. We design a numerical scheme for solving the Multi-step Forward Dynamic Programming (MDP) equation arising from the time-discretization of backward stochastic differential equations. The ...
JSTOR Daily

On Residual Empirical Processes of Stochastic Regression Models with Applications to Time Series

Motivated by Gaussian tests for a time series, we are led to investigate the asymptotic behavior of the residual empirical processes of stochastic regression models. These models cover the fixed ...
Nature

Cox proportional hazards survival regression in haplotype-based association analysis using the Stochastic-EM algorithm

It is now widely recognized that haplotype information inferred from genotypes can be of great interest to better characterize the role of a candidate gene in the etiology of a complex trait in the ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using pseudo-inverse training. Compared to other training techniques, such as stochastic gradient descent, ...
Nature

Option Pricing and Stochastic Control Methods

Option pricing and stochastic control methods constitute a vital intersection of quantitative finance and applied mathematics, offering robust frameworks for evaluating derivative securities and ...