Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression

ESTIMATING THE PARAMETERS OF A FUZZY LINEAR REGRESSION MODEL

Fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. Several methods for evaluating fuzzy coefficients in linear regression models have been proposed. The first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. In this the...

Estimating Regression Effects in Com Poisson Generalized Linear Model

Com Poisson distribution is capable of modeling the count responses irrespective of their mean variance relation and the parameters of this distribution when fitted to a simple cross sectional data can be efficiently estimated using maximum likelihood (ML) method. In the regression setup, however, ML estimation of the parameters of the Com Poisson based generalized linear model is computational...

Bayes Linear Covariance Matrix Adjustment for Multivariate Dynamic Linear Models

A methodology is developed for the Bayes linear adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a distribution-free inner-product space of matrix objects which facilitates such adjustment. This approach helps to make the analysis simple, tractable and robust. To illustrate the methods, a simple mo...

Bayesian regression with multivariate linear splines

estimating the parameters of a fuzzy linear regression model

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...