The Allometry of Coarse Root Biomass: Log-Transformed Linear Regression or Nonlinear Regression?

The Allometry of Coarse Root Biomass: Log-Transformed Linear Regression or Nonlinear Regression?

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for sca...

Unit Root Log Periodogram Regression By

Log periodogram (LP) regression is shown to be consistent and to have a mixed normal limit distribution when the memory parameter d 1⁄4 1. Gaussian errors are not required. The proof relies on a new result showing that asymptotically infinite collections of discrete Fourier transforms (dft’s) of a short memory process at the fundamental frequencies in the vicinity of the origin can be treated a...

Log - Linear Modeling and Logistic Regression

Linear regression, the normal distribution of error values or normal distribution of the dependent variable?

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Erratum to: Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

BACKGROUND Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. METHODS We derived a set of formulae to transform a...