The Linear Template Fit
نویسندگان
چکیده
The estimation of parameters from data is a common problem in many areas the physical sciences, and frequently used algorithms rely on sets simulated which are fit to data. In this article, an analytic solution for simulation-based parameter problems presented. matrix formalism, termed Linear Template Fit, calculates best estimators interest. It combines linear regression with method least squares. algorithm uses only predictions calculated few values interest, have been made available prior its execution. Fit particularly suited performance critical applications computationally intense simulations, otherwise often limited their usability statistical inference. Equations error propagation discussed detail given closed form. For nonlinear dependence Quadratic introduced. As example application, determination strong coupling constant inclusive jet cross section at CERN Large Hadron Collider studied compared previously published results.
منابع مشابه
on the effect of linear & non-linear texts on students comprehension and recalling
چکیده ندارد.
15 صفحه اولM-smoother with local linear fit
Local linear M-smoothing is proposed as a method for noise reduction in one-dimensional (1D) signals. It is more appropriate than conventional local linear smoothing, because it does not introduce blurring of jumps in the signal. It improves local constant Msmoothing, by avoiding boundary effects at edges and jumps. While the idea of local linear M-smoothing is straightforward, numerical issues...
متن کاملSAS Software to Fit the Generalized Linear Model
In recent years, the class of generalized linear models has gained popularity as a statistical modeling tool. This popularity is due in part to the flexibility of generalized linear models in addressing a variety of statistical problems and to the availability of software to fit the models. The SAS system provides two new tools that fit generalized linear models. The GENMOD procedure in SAS/ST...
متن کاملParametric Piecewise Linear Models with Good Fit to the Data
This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). The point sets can be obtained as edge pixels in digital images or from a laser range scanner mounted on a moving robot. We point out two problems with the maximum likelihood estimate (MLE) that present serious dr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: European Physical Journal C
سال: 2022
ISSN: ['1434-6044', '1434-6052']
DOI: https://doi.org/10.1140/epjc/s10052-022-10581-w