Least square fitting with one explicit parameter less

Abstract It is shown that whenever the multiplicative normalization of a fitting function is not known, least square fitting by ^(χ2)minimization can be performed with one parameter less than usual by converting the normalization parameter into a function of the remaining parameters and the data. Title of program: FITM1 Catalogue Id: AEYG_v1_0 Nature of problem Least square minimization when one of the free parameters is the multiplicative normalization of the fitting function. Versions of this program held in the CPC repository in Mendeley Data AEYG_v1_0; FITM1; 10.1016/j.cpc.2015.09.021 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

摘要：研究表明，当拟合函数的乘法归一化因子未知时，可通过将归一化参数转化为其余参数与数据的函数，采用比常规少一个参数的方式完成基于χ²最小化的最小二乘拟合。
程序名称：FITM1
目录标识：AEYG_v1_0
问题属性：当某一自由参数为拟合函数的乘法归一化因子时的最小二乘最小化问题。
Mendeley数据集中CPC仓库内的本程序版本：AEYG_v1_0; FITM1; 10.1016/j.cpc.2015.09.021
本程序源自贝尔法斯特女王大学馆藏的CPC程序库（1969-2018年）。