Analysing effects of meteorological variables on weather codes by logistic regression

Generating Probabilities From Numerical Weather Forecasts by Logistic Regression

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio equal to a linear combination of the inputs. As any statistical model, logistic models will suffer from over-fitting if the number of inputs is comparable to th...

Phylogenetic logistic regression for binary dependent variables.

We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an evolutionary model of binary traits in which trait values switch between 0 and 1 as species evolve up a phylo...

Efficiencies and Surrogate Variables in Logistic Regression

Raymond J. Carroll Department of Statistics Texas A&M University College Station, TX 77843 We study logistic regression with response y when the true predictor x is measured with· error and the observable data consist of pairs (y,w), where w is correlated with x. Two approaches to estimation are studied. In the first, integrated likelihood estimates are obtained from the conditional distributio...

A Purposeful Selection of Variables Macro for Logistic Regression

The main problem in any model-building situation is to choose from a large set of covariates those that should be included in the “best” model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms embedded in SAS PROC LOGISTIC. Those methods are mechanical and as such carry some limitations. Hosmer...

Notes on Logistic Regression