The logistic regression model is the workhorse of epidemiological data analysis. The model helps to clarify the relationship between multiple exposures and a binary outcome. Logistic regression analysis is readily implemented using existing statistical software, and this has contributed to it becoming a routine procedure for epidemiologists. In this paper, the authors focus on a causal model wh...

Heavy tail probability distributions are important in many scientific disciplines such as hydrology, geology, and physics and therefore feature heavily in statistical practice. Rather than specifying a family of heavy-tailed distributions for a given application, it is more common to use a nonparametric approach, where the distributions are classified according to the tail behavior. Through the...

This paper gives algorithms for determining isotonic median regressions (i.e., isotonic regression using the L1 metric) satisfying order constraints given by various ordered sets. For a rooted tree the regression can be found in Θ(n log n) time, while for a star it can be found in Θ(n) time, where n is the number of vertices. For bivariate data, when the set is a grid the regression can be foun...

test day milk records of a holstein dairy farm collected during years 2004 to 2006 on 142 cows in the 1st lactation vs. 140 cows in the 2nd lactation were used in this research. there were real milk yields of cows available in each part of lactation in this farm making it possible to compare functions based on the new criteria (squared error of estimation). five functions were compared to estim...

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...

Problems encountered in multiple regression due to multicolinearity or missing data can be overcome by using PLS regression. Several versions of the PLS regression algorithm exist. In this paper, we present a new version of this algorithm which can be extended to generalized linear regression models such as ordinal or multinomial logistic regression, generalized linear models, and Cox regressio...