000			nam a22 4500
999			_c32819 _d32819
008			240219b xxu||||| |||| 00| 0 eng d
020			_a9781441998156
082			_a519.5 _bCHR
100			_aChristensen, Ronald
245			_aPlane Answers to Complex Questions : The Theory of Linear Models
250			_a4th ed.
260			_bSpringer, _aNew York : _c2011
300			_axxi, 494 p. ; _bill. , _c24 cm
365			_b84.99 _c€ _d94.80
490			_aSpringer Texts in Statistics
504			_aIncludes bibliographical references and index.
520			_aThe third edition of Plane Answers includes fundamental changes in how some aspects of the theory are handled. Chapter 1 includes a new section that introduces generalized linear models. Primarily, this provides a defini­ tion so as to allow comments on how aspects of linear model theory extend to generalized linear models. For years I have been unhappy with the concept of estimability. Just because you cannot get a linear unbiased estimate of something does not mean you cannot estimate it. For example, it is obvious how to estimate the ratio of two contrasts in an ANOVA, just estimate each one and take their ratio. The real issue is that if the model matrix X is not of full rank, the parameters are not identifiable. Section 2.1 now introduces the concept of identifiability and treats estimability as a special case of identifiability. This change also resulted in some minor changes in Section 2.2. In the second edition, Appendix F presented an alternative approach to dealing with linear parametric constraints. In this edition I have used the new approach in Section 3.3. I think that both the new approach and the old approach have virtues, so I have left a fair amount of the old approach intact. Chapter 8 contains a new section with a theoretical discussion of models for factorial treatment structures and the introduction of special models for homologous factors.
650			_aQuadratic forms
650			_aMahalanobis distance
650			_aEstimable functions
650			_aDesign matrix
650			_aCovariance matrix
942			_2ddc _cBK