This monograph deals with Introduction, Basic Concepts, Brief Review of Regression Theory. It present the work related to Least Absolute Deviations (LAD) Regression and its estimation theory with and without auto correlated errors. LAD and Least Squares estimation of censored regression model with fixed and marginal effects are also discussed. Further, it contains LAD estimation for linear and nonlinear regression model for truncated and censored data.

Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation discusses generalized properties of Lp-estimators. Includes an algorithm for identifying outliers using least absolute value criterion in regression modeling reviews redescending M-estimators studies Li linear regression proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model summarizes known properties of Li estimators for time series analysis examines ordinary least squares, latent root regression, and a robust regression weighting scheme and evaluates results from five different robust ridge regression estimators.