Author: A. ChaturvediPublisher:
ISBN: 9780864181619
Category : Estimation theory
Languages : en
Pages : 11
Book Description
Improved Estimation of the Linear Regression Model with Autocorrelated Errors
Author: A. ChaturvediPublisher:
ISBN: 9780864181619
Category : Estimation theory
Languages : en
Pages : 11
Book Description
Improved Estimation of the Disturbance Variance in a Linear Regression Model
Author: Stanford University. Department of StatisticsPublisher:
ISBN:
Category :
Languages : en
Pages : 13
Book Description
Estimating the Autocorrelated Error Model with Trended Data, Further Results
Author: Rolla Edward ParkPublisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 54
Book Description
A Monte Carlo study is made of the small sample properties of various estimators of the linear regression model with first-order autocorrelated errors. When independent variables are trended, estimators using T transformed observations (Prais-Winsten) are much more efficient than those using T-1 (Cochrane-Orcutt). The best of the feasible estimators is iterated Prais-Winsten using a sum-of-squared-error minimizing estimate of the autocorrelation coefficient rho. None of the feasible estimators performs well in hypothesis testing; all seriously underestimate standard errors, making estimated coefficients appear to be much more significant than they actually are. (Author).
Improved Estimation for Linear Models Under Different Loss Functions
Author: Zahirul HoquePublisher:
ISBN:
Category : Linear models (Statistics)
Languages : en
Pages : 344
Book Description
This thesis investigates improved estimators of the parameters of the linear regression models with normal errors, under sample and non-sample prior information about the value of the parameters. The estimators considered are the unrestricted estimator (UE), restricted estimator (RE), shrinkage preliminary test estimator (SPTE), and shrinkage estimator (SE). The performance of the estimators are investigated with respect to bias, squared error and linex loss. For the analyses of the risk functions of the estimators, analytical, graphical and numerical procedures are adopted.
Prediction and Improved Estimation in Linear Models
Author: John BibbyPublisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 200
Book Description
Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.
Estimation of Regression Parameters in Linear Regression Model with Autocorrelated Errors
Author: Thuan Van TruongPublisher:
ISBN:
Category : Autocorrelation (Statistics)
Languages : en
Pages : 218
Book Description
On Lindley-like Mean Correction in the Improved Estimation of Linear Regression Models
Author: V. K. SrivastavaPublisher:
ISBN: 9780867461916
Category : Estimation theory
Languages : en
Pages : 14
Book Description
Efficient Estimation of Linear Regression Models with Autocorrelated Errors
Author: Mathew James MoreyPublisher:
ISBN:
Category : Autocorrelation (Statistics)
Languages : en
Pages : 328
Book Description
Introduction to Linear Regression Analysis
Author: Douglas C. MontgomeryPublisher: Wiley-Interscience
ISBN:
Category : Computers
Languages : en
Pages : 680
Book Description
A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of: ? Indicator variables, making the connection between regression and analysis-of-variance modelss ? Variable selection and model-building techniques ? The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures ? Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation ? Generalized linear models The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.
Author: Andrew L. Rukhin