000			02631nam a22002177a 4500
005			20241114194958.0
008			241114b |||||||| |||| 00| 0 eng d
020			_a9780000988843
082			_a519.536 _bSEN
100			_aSengupta, Debasis _918294
245			_aLinear models and regression with R: _ban integrated approach
260			_bWorld Scientific Publishing _aSingapore _c2023
300			_axxi, 750 p.
365			_aINR _b1695.00
490			_aSeries on Multivariate analysis; Vol. 11
520			_aStarting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed. The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references. This completely updated and new edition of Linear Models: An Integrated Approach includes the following features: Applications with data sets, and their implementation in R, Comprehensive coverage of regression diagnostics and model building, Coverage of other special topics such as collinearity, stochastic and inequality constraints, misspecified models, etc., Use of simple statistical ideas and interpretations to explain advanced concepts, and simpler proofs of many known results, Discussion of models covering mixed-effects/variance components, spatial, and time series data with partially unknown dispersion matrix, Thorough treatment of the singular linear model, including the case of multivariate response, Insight into updates in the linear model, and their connection with diagnostics, design, variable selection, Kalman filter, etc., Extensive discussion of the foundations of linear inference, along with linear alternatives to least squares. (https://www.worldscientific.com/worldscibooks/10.1142/11282#t=aboutBook)
650			_aLinear model _918659
650			_aRegression _918660
700			_aJammalamadaka, Sreenivasa Rao _918661
942			_cBK _2ddc
999			_c7760 _d7760