The tree based linear regression model for hierarchical categorical variables

Role of Categorical Variables in Multicollinearity in the Linear Regression Model

The present article discusses the role of categorical variable in the problem of multicollinearity in linear regression model. It exposes the diagnostic tool condition number to linear regression models with categorical explanatory variables and analyzes how the dummy variables and choice of reference category can affect the degree of multicollinearity. Such an effect is analyzed analytically a...

Linear regression model with histogram-valued variables

Histogram-valued variables are a particular kind of variables studied in Symbolic Data Analysis where to each entity under analysis corresponds a distribution that may be represented by a histogram or by a quantile function. Linear regression models for this type of data are necessarily more complex than a simple generalization of the classical model: the parameters cannot be negative; still th...

A multiple linear regression model for LR fuzzy random variables

In standard regression analysis the relationship between one (response) variable and a set of (explanatory) variables is investigated. In a classical framework the response is affected by probabilistic uncertainty (randomness) and, thus, treated as a random variable. However, the data can also be subjected to other kinds of uncertainty, such as imprecision. A possible way to manage all of these...

Regression models for categorical dependent variables using Stata, 2nd Edition

Stein Type Estimators for Disturbance Variance in Linear Regression Model

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