000			04272cam a2200409 i 4500
001			000703536
005			20240105153153.0
008			171122t20022002nyua b 001 0 eng d
035			_a424454
040			_aUIASF _bspa _erda _cUIASF _dUIASF
050		4	_aQA 276 _bM94.2002
100	1		_aMyers, Raymond H _eautor
245	1	0	_aGeneralized linear models : _bwith applications in engineering and the sciences / _cRaymond H. Myers, Douglas C. Montgomery, G. Geoffrey Vining.
264		1	_aNew York : _bJ. Wiley, _c[2002],
264		4	_c©2002
300			_axiii, 342 páginas : _bilustraciones ; _c24 cm.
336			_atexto _btxt _2rdacontent
337			_asin mediación _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
490	1		_aWiley series in probability and statistics
500			_a"A Wiley-Interscience publication."
504			_aIncluye referencias bibliográficas (páginas 334-338) e índice
505	0		_a1. Introduction to Generalized Linear Models. 1.1. Linear Models. 1.2. Nonlinear Models. 1.3. The Generalized Linear Model -- 2. Linear Regression Models. 2.1. The Linear Regression Model and Its Application. 2.2. Multiple Regression Models. 2.3. Parameter Estimation Using Maximum Likelihood. 2.4. Model Adequacy Checking. 2.5. Parameter Estimation by Weighted Least Squares -- 3. Nonlinear Regression Models. 3.1. Linear and Nonlinear Regression Models. 3.2. Transforming to a Linear Model. 3.3. Parameter Estimation in a Nonlinear System. 3.4. Statistical Inference in Nonlinear Regression. 3.5. Weighted Nonlinear Regression. 3.6. Examples of Nonlinear Regression Models -- 4. Logistic and Poisson Regression Models. 4.1. Regression Models Where the Variance Is a Function of the Mean. 4.2. The Logistic Regression Model. 4.3. Parameter Estimation Using Maximum Likelihood. 4.4. Different Forms of Statistical Inference Using Logistic Regression. 4.5. Examples Using Logistic Regression. 4.6. Other Considerations in Logistic Regression. 4.7. The Concept of Overdispersion in Logistic Regression. 4.8. Introduction to Poisson Regression. 4.9. Maximum Likelihood Estimators for Poisson Regression. 4.10. Applications in Poisson Regression. 4.11. Examples Using Poisson Regression. 4.12. Classification Variables and Extensions to the Anova Model -- 5. The Family of Generalized Linear Models. 5.1. The Exponential Family of Distributions. 5.2. Formal Structure for the Class of Generalized Linear Models. 5.3. Likelihood Equations for Generalized Linear Models. 5.4. Quasi-likelihood. 5.5. Other Important Distributions for Generalized Linear Models. 5.6. A Class of Link Functions -- The Power Function. 5.7. Inference and Residual Analysis for Generalized Linear Models. 5.8. Examples with the Gamma Distribution -- 6. Generalized Estimating Equations. 6.1. Data Layout for Longitudinal Studies. 6.2. Impact of the Correlation Matrix R. 6.3. Iterative Procedure in the Normal Case,
505	0		_aIdenti Link. 6.4. Generalized Estimating Equations for More Generalized Linear Models -- 7. Further Advances and Applications in GLM. 7.1. Introduction. 7.2. Experimental Designs for Generalized Linear Models. 7.3. Quality of Asymptotic Results and Related Issues. 7.4. GLM Analysis of Screening Experiments. 7.5. GLM and Data Transformation. 7.6. Modeling Both a Process Mean and Process Variance Using GLM. 7.7. Generalized Additive Models -- App. A.1. Background on Basic Test Statistics -- App. A.2. Background from the Theory of Linear Models -- App. A.3. The Gauss-Markov Theorem, var([epsilon]) = [sigma][superscript 2]I -- App. A.4. The Relationship Between Maximum Likelihood Estimation of the Logistic Regression Model and Weighted Least Squares -- App. A.5. Computational Details for GLMs for a Canonical Link -- App. A.6. Computational Details for GLMs for a Noncanonical Link.
650		0	_aLinear models (Statistics)
650		4	_aModelos lineales (Estadística)
700	1		_aMontgomery, Douglas C _eautor
700	1		_aVining, G. Geoffrey, _d1954- _eautor
830		0	_aWiley series in probability and statistics
905			_a01
942		1	_cNEWBFXC1
999			_c659169 _d659169
980			_851 _gRonald RUIZ