Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Estimating Litter Decomposition Rate in Single-Pool Models Using Nonlinear Beta Regression

Litter decomposition rate (k) is typically estimated from proportional litter mass loss data using models that assume constant, normally distributed errors. However, such data often show non-normal errors with reduced variance near bounds (0 or 1), potentially leading to biased k estimates. We compared the performance of nonlinear regression using the beta distribution, which is well-suited to ...

Estimating Rate Equations Using Nonparametric Regression Methods

quantifying of germination rate response to temperature of three sunflower varieties using nonlinear regression models

background and objectives: germination and seedling establishment are two critical and important stages during plants growing season. from seed technology viewpoint, germination refers to emergence and development from seed embryo, so that could produce normal seedling. many studies  have been done to determine germination cardinal temperatures and required thermal time for emergence occurrence...

A novel nonlinear programming approach for estimating CAPM beta of an asset using fuzzy regression

Article history: Received 3 August 2014 Received in revised form 27 September 2014 Accepted 24 October 2014 Available online 3 November 2014

Estimating nonlinear regression errors without doing regression

A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N. Comparing the predicted residual errors with those derived from a linear model assumption provides...