In this some new estimation methods and testing procedures for the linear regression models with heteroscedastic disturbances. A Minimum Norm Quadratic Unbiased (MINQU) estimation method has been developed for estimating the unknown heteroscedastic error variances by using the weighted studentized residuals. A multiplicative heteroscedastic linear regression model has been specified and a method of estimating the parameters of linear regression model along with the in the heteroscedastic error variance has been given by using the predicted residuals. Three types of modified estimators ...

This book has brought out some procedures to construct statistical models for Customer Relationship Management (CRM) by using Univariate and Multiple Logistic regression models. It contains the theorem, concepts and evaluation of the CRM. The various problems with CRM and their remedies have been described. Different aspects of CRM strategies and the implementation of CRM have been discussed along with the solutions. Data mining in CRM along with Data Mining techniques have been presented in this book. The procedures for the assessment of the fitted logistic regression models for CRM by using...

This book has brought out inferential methods to forecasting with linear statistical and time series models, the various forecasting methods existing in the literature have been briefly reviewed with inferential problems on them. In view of the importance of forecasting is empirical research, some new procedures for applied forecasting have been developed.Here, these techniques are developed by using Internally Studentized Residuals. Further, a modified Box-Jenkins methodology has been presented for auto Integrated Moving average model ARIMA(p,d,q) based on Internally Studentized Residuals....

In this book, an attempt has been made to propose some new estimation procedures for the linear regression models such as elemental slopes methods, Grouping method, Hat diagonals method, and Dispersion and Correlation methods for estimating for estimating the parameters of the linear regression models. The proposed methods have been applied to two and three variables linear models for their validity.In this book Introduction given in the Chapter I, some important existing estimation procedures such as OLS, GLS, WLS, RLS and ML have been described in Chapter II. A brief about the review of the...

In the present book, Chapter-I is an introductory one. It gives general introduction about the nonlinear regression models. A brief review about the existing inferential procedures for nonlinear regression models has been give in Chapter-II. It contains various nonlinear methods, of estimation based on nonlinear least squares and maximum likelihood methods, besides the methods by using some numerical analysis procedures.Chapter-II and IV describe the specification and estimation of some important nonlinear production function models such as Cobb-Douglas, Constant Elasticity of Substitution...

This book has brought out some applications of linear models by using various concepts in the Linear Algebra.Most of the Applied Regression analysis techniques are based in the concept of linear model.It describes the applications of some advanced concepts in the matrix theory to linear models.The specification, estimation and various inferential aspects of linear statistical models have been discussed.The various problems of Mathematical and Statistical linear models have been presented in this book.It contains some applications of generalized Inverse matrices to the linear models

Forecasting is an important aid in effective and efficient planning. It is a current topic of growing important in business and economic analysis. It is an attempt is predict the future by examining the past. It consists of generating unbiased estimates of future magnitude of some variable, on the basis of past and present knowledge and experience. The present work of the reasearch is focused on development of some forecasting methods with special reference to Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) methods along with residual measures. The...

In the present book, Chapter I gives the introduction about the concept of outliers along with the statistical inference in linear regression model. The various test statistics for detecting outliers such as Maximum Normed Residual, Extreme Studentized Deviation, Studentized Range, Kurtosis, R-Statistic, Maximum Eigen differences Least Medium Squares (LMS) estimator, Mahalanobis Distance, Cooks Distance, DFFITS, DF BETAS, COVRATIO, Scale ratio, Gap Test Statistic and 2-sigma Region have been described in Chapter II. Different test procedures to detect the outliers have been reviewed in...

In this book some mathematical and statistical models have been specified for forecasting and proposed certain criteria for choosing an appropriate forecasting model.the general method of forecasting by using regression model with the estimates of the parameters of the general linear statistical model has been described along with the estimates of the parameters of the general linear statistical model has been described along with the properties of the forecasts.Different stationary and non stationary autoregressive and moving averege processes such as AR(1),AR(2),ARMA(p,q) and ARMA(p,d,q)...

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...