Modal parameter estimation with multi-reference curve fitting.

Autonomous Modal Parameter Estimation: Methodology

Traditionally, the estimation of modal parameters from a set of measured data has required significant experience. However, as the technology has matured, increasingly, analysis is being performed by less experienced engineers or technicians. To address this development, frequently software solutions are focusing upon either wizard-based or autonomous/semiautonomous approaches. A number of auto...

On parameter estimation in the bass model by nonlinear least squares fitting the adoption curve

The Bass model is one of the most well-known and widely used first-purchase diffusion models in marketing research. Estimation of its parameters has been approached in the literature by various techniques. In this paper, we consider the parameter estimation approach for the Bass model based on the nonlinear weighted least squares fitting of its derivative known as the adoption curve. We show th...

Parameter estimation techniques: a tutorial with application to conic fitting

Almost all problems in computer vision are related in one form or an other to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter es timation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormal ...

B-Fitting: An Estimation Technique with Automatic Parameter Selection

The problem of model selection is endemic in the machine vision literature, yet largely unsolved in the statistical field. Our recent work on a theoretical statistical evaluation of the Bhattacharyya similarity metric has led us to conclude that this measure can be used to provide a solution. Here we describe how the approach may be extended to solve the problem of model selection during the fu...

The best parameter subset using the Chebychev curve fitting criterion