Linear Regression Models under Multicollinearity Год выпуска: 2013Автор: M. Pushpalatha,M. Bhupathi Naidu and Balasiddamuni PagadalaИздательство: LAP Lambert Academic PublishingСтраниц: 216ISBN: 9783659389764Описание 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.
Похожие книги
Benjamin Kedem, Konstantinos Fokianos. Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics). – М.: , 0. – 0 с. T. Fomby. Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later (Advances in Econometrics). – М.: , 2003. – 0 с. Michael H. Kutner. MP Applied Linear Regression Models with Student CD-rom. – М.: , 2003. – 0 с. Matteo Ranaboldo. Multiple linear regression MOS for short-term wind power forecast. – М.: LAP Lambert Academic Publishing, 2013. – 52 с. S. Durga Prasad,Balasiddamuni Pagadala and Ramesh Mummineni. Statistical Inference In Time Series Regression Models. – М.: LAP Lambert Academic Publishing, 2013. – 212 с. K.V.S.D.P. Vara Prasad,Balasiddamuni Pagadala and R.V.S.S. Nagabhushana Rao. Testing Restrictions In Linear Statistical Models. – М.: LAP Lambert Academic Publishing, 2014. – 116 с. K. Sreenivasulu,V.H. Bajaj and Balasiddamuni Pagadala. Linear Regression Models with Heteroscedastic Errors. – М.: LAP Lambert Academic Publishing, 2013. – 268 с. M. Pushpalatha,M. Bhupathi Naidu and Balasiddamuni Pagadala. Linear Regression Models under Multicollinearity. – М.: LAP Lambert Academic Publishing, 2013. – 216 с. Stella Ingileela,M. Subbarayudu and Balasiddamuni Pagadala. On Some Aspects Of Residual Analysis In Regression Models. – М.: LAP Lambert Academic Publishing, 2013. – 136 с. Kandunuru Vijaya Kuma,Balasiddamuni Pagadala and Theertham Gangaram. Testing Normality In Linear Statistical Models. – М.: LAP Lambert Academic Publishing, 2014. – 144 с. R.V.S. Prasad,Balasiddamuni Pagadala and C.L. Kantha Rao. Estimation of Linear Models Under Heteroscedasticity. – М.: LAP Lambert Academic Publishing, 2014. – 164 с. M.V. Chalapathi Rao,Balasiddamuni Pagadala and J. Prabhakara Naik. Inference in Linear Models With Auto Correlated Disturbances. – М.: LAP Lambert Academic Publishing, 2014. – 144 с. K.N. Sreenivasulu,M. Subbarayudu and Balasiddamuni Pagadala. Some Aspects of Estimation Procedures in Regression Models. – М.: LAP Lambert Academic Publishing, 2013. – 116 с. D. Giri,Balasiddamuni Pagadala and Pagadala Srivyshnavi. Some New Selection Techniques For Linear Statistical Models. – М.: LAP Lambert Academic Publishing, 2013. – 128 с. C. Ramesh Reddy,Balasiddamuni Pagadala and Pedda Redappa Reddy. Residuals and Their Applications in Econometrics. – М.: LAP Lambert Academic Publishing, 2014. – 108 с. N. Ramesh Kumar,Balasiddamuni Pagadala and A.V. Prasad. On Criteria for Testing Linear Hypotheses in Regression Models. – М.: LAP Lambert Academic Publishing, 2014. – 76 с. Bushra Abdalrasool Ali. Biased Estimators for the parameters of Linear Regression model. – М.: LAP Lambert Academic Publishing, 2013. – 96 с. Образцы работ
Задайте свой вопрос по вашей теме
Контакты
Поделиться
Мы в социальных сетях
Реклама
Отзывы
Кэтрин Я вам всем обязана в плане диплома. И хотела бы под конец разойтись на положительной ноте. И отблагодарить за терпение, Марина.
