Incorporating missingness for estimation of marginal regression models with multiple source predictors

Derivation of regression models for pan evaporation estimation

Evaporation is an essential component of hydrological cycle. Several meteorologicalfactors play role in the amount of pan evaporation. These factors are often related to eachother. In this study, a multiple linear regression (MLR) in conjunction with PrincipalComponent Analysis (PCA) was used for modeling of pan evaporation. After thestandardization of the variables, independent components were...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Shrinkage Estimation of Regression Models with Multiple Structural Changes

In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain esti...

Marginal Regression Models with VaryingCoeÆ ients for Correlated Ordinal

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...