The GAMLSS model assumes that the distribution of response variable has any parametric distribution which might be heavy or light-tailed, and positively or negatively skewed.
32.
In a regression model setting, the goal is to establish whether or a not a relationship exists between a response variable and a set of predictor variables.
33.
It is particularly useful when the response variable is categorical, binary or subject to a constraint ( e . g . only positive responses make sense ).
34.
If the independent variable is referred to as an " explanatory variable " then the term " response variable " is preferred by some authors for the dependent variable.
35.
Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.
36.
Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.
37.
The closer this value is to 1.0, the better the data fit to a hyperplane representing the relationship between the response variable and a set of covariate variables.
38.
Note : This type of fit, with the response variable appearing on both sides of the function, should only be used to obtain starting values for the nonlinear fit.
39.
A main assumption in linear regression is constant variance or ( homoscedasticity ), meaning that different response variables have the same variance in their errors, at every predictor level.
40.
For random censoring on the response variables, the censored quantile regression of Portnoy ( 2003 ) provides consistent estimates of all identifiable quantile functions based on reweighting each censored point appropriately.
