In this book, an attempt has been made by proposing some new inferential procedures for linear regression models with different autoregressive schemes for disturbances. These estimation procedures have used iterative methods based on studentized residuals. It proposes some new inferential methods for linear statistical models with first, second and fourth order autoregressive disturbances. A new estimated iterative restricted GLS estimator has been derived for linear regression model with first order autoregressive disturbances. Later it has been applied for testing the general linear hypothesis. The linear statistical models have been specified with AR (1), AR (2) and AR (4) disturbances. The EGLS methods of estimation have been developed with particular AR (2) and AR (4) disturbances by using Iterative procedures. Here, Studentized residuals have been used in the place of OLS residuals. The parametric tests for particular second order and fourth order autocorrelations also have been discussed in this book

This book proposes the various types of new Ridge regression estimators to deal with the problem of multicollinearity in multiple linear regression analysis.An Ordinary ridge regression estimators and an orthonormal( ridge regression estimators have been derived by selecting the values for ridge parameter based on studentized residuals.A partitioned linear regression model has been specified and the ridge regression estimator has been developed by using Internally studentized residual sum of squares.besides these, an Adaptive General Ridge regression estimator's and a new combined restricted ridge regression estimators have been proposed along with iterative procedures for the solutions of elements of ridge parameters matrix.