We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and quasi-likelihood contexts. In the ordinary ...

Polynomial interpolation to analytic functions can be very accurate, depending on the distribution of the interpolation nodes. However, in equispaced nodes and the like, besides being badly ...

Abstract: In this paper we extend the domain of applicability of the E-method [7, 8], as a hardware-oriented method for evaluating elementary functions using polynomial and rational function ...

Abstract: The problem of asymptotic state reconstruction of systems characterized by nonlinear state-output maps is investigated in this paper. The approach here presented is based on the ...

This repository contains Python code for performing polynomial regression using gradient descent, implemented with PyTorch, as required for Assignment 1. The code provides functions for polynomial ...

ABSTRACT: The accelerated failure time partial linear model allows the functional form of the effect of covariates to be possibly nonlinear and unknown. We propose to approximate the nonparametric ...

Three hours of lecture/discussion per week. Algebraic operations on polynomials and rational functions as expressions, in equations, or inequalities. Graphing of linear and polynomial equations. An ...

ABSTRACT: The accelerated failure time partial linear model allows the functional form of the effect of covariates to be possibly nonlinear and unknown. We propose to approximate the nonparametric ...

Thus {f, g} represents the unordered pair of functions f and g. So will plot both f and g on the same graph. We don't care wich order it does them in so we can give f and g as an unordered pair.