Logistic regression is one of statistical methods that used to analyze the correlation between categorical response variables and predictor which are or continuous. Many studies on logistic have been carried out by assuming variable its logit link function a linear relationship. However, in several cases it was found relationship not always linear, but could be quadratic, cubic, form other curv...

the efficiency of three statistical models, ahp surface-weighted density bivariate (semi-quantitative models), stepwise multivariate regression and logistic multivariate regression models were compared in chehel-chai watershed in golestan province, iran. in current study the hazard map was prepared according to the top model of landslide hazard map. chehel-chai watershed is located as one of go...

We introduce a scale invariance property for Poisson point processes and use this property to define a series representation for a correlated bivariate gamma process. This approach is quite general and can be used to define other types of multidimensional Lévy processes with given marginals. Some important special cases are bivariate G-processes, bivariate variance gamma processes and multivari...

The Poisson regression model is frequently used to analyze count data. Pseudo R-squared measures for Poisson regression models have recently been proposed and bias adjustments recommended in the presence of small samples and/or a large number of covariates. In practice, however, data are often overor sometimes even underdispersed as compared to the standard Poisson model. The de5nition of Poiss...

While Poisson regression is a popular tool for modeling count data, it is limited by its associated model assumptions. One assumption is that the response variable follows a Poisson distribution. However, overor under-dispersion are common in practice and are not accommodated by Poisson regression. In addition, the dispersion is assumed fixed across observations, whereas in practice dispersion ...