Tabla de Contenidos:

Tabla de Contenidos: “…8.2 The Method of Steepest Descent8.3 Analysis of Gradient Methods; Exercises; 9 Newton's Method; 9.1 Introduction; 9.2 Analysis of Newton's Method; 9.3 Levenberg-Marquardt Modification; 9.4 Newton's Method for Nonlinear Least Squares; Exercises; 10 Conjugate Direction Methods; 10.1 Introduction; 10.2 The Conjugate Direction Algorithm; 10.3 The Conjugate Gradient Algorithm; 10.4 The Conjugate Gradient Algorithm for Nonquadratic Problems; Exercises; 11 Quasi-Newton Methods; 11.1 Introduction; 11.2 Approximating the Inverse Hessian; 11.3 The Rank One Correction Formula; 11.4 The DFP Algorithm…”

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Tabla de Contenidos: “…Software programs dealing with dynamic forward and inverse modeling problems in food science; 2.6. Future trends; References; Chapter 3: Modeling thermal processing and reactions; 3.1. …”

Tabla de Contenidos: “…Motivating example: interleukin-1 and cardiovascular diseases -- 5.2. Inverse-variance weighted method -- 5.3. Heterogeneity and pleiotropy -- 5.4. …”

Tabla de Contenidos: “…256 -- 6.17.2 Consistency of equivariant estimators 256 -- 6.17.3 The estimating equations of the MLE 257 -- 6.17.4 Asymptotic BP of monotone M-estimators 258 -- 6.17.5 The estimating equations for S-estimators 260 -- 6.17.6 Behavior of S-estimators for high p 261 -- 6.17.7 Calculating the asymptotic covariance matrix of location M-estimators 262 -- 6.17.8 The exact fit property 263 -- 6.17.9 Elliptical distributions 264 -- 6.17.10 Consistency of Gnanadesikan-Kettenring correlations 265 -- 6.17.11 Spherical principal components 266 -- 6.17.12 Fixed point estimating equations and computing algorithm for the GS estimator 267 -- 6.18 Recommendations and software 268 -- 6.19 Problems 269 -- 7 Generalized Linear Models 271 -- 7.1 Binary response regression 271 -- 7.2 Robust estimators for the logistic model 275 -- 7.2.1 Weighted MLEs 275 -- 7.2.2 Redescending M-estimators 276 -- 7.3 Generalized linear models 281 -- 7.3.1 Conditionally unbiased bounded influence estimators 283 -- 7.4 Transformed M-estimators 284 -- 7.4.1 Definition of transformed M-estimators 284 -- 7.4.2 Some examples of variance-stabilizing transformations 286 -- 7.4.3 Other estimators for GLMs 286 -- 7.5 Recommendations and software 289 -- 7.6 Problems 290 -- 8 Time Series 293 -- 8.1 Time series outliers and their impact 294 -- 8.1.1 Simple examples of outliers influence 296 -- 8.1.2 Probability models for time series outliers 298 -- 8.1.3 Bias impact of AOs 301 -- 8.2 Classical estimators for AR models 302 -- 8.2.1 The Durbin-Levinson algorithm 305 -- 8.2.2 Asymptotic distribution of classical estimators 307 -- 8.3 Classical estimators for ARMA models 308 -- 8.4 M-estimators of ARMA models 310 -- 8.4.1 M-estimators and their asymptotic distribution 310.…”

Tabla de Contenidos: “…The Mathematics of Infinity: A Guide to Great Ideas; Contents; Preface for the Second Edition; 1 Logic; 1.1 Axiomatic Method; 1.2 Tabular Logic; 1.3 Tautology; 1.4 Logical Strategies; 1.5 Implications From Implications; 1.6 Universal Quantifiers; 1.7 Fun With Language and Logic; 2 Sets; 2.1 Elements and Predicates; 2.2 Equality; 2.3 Cartesian Products; 2.4 Power Sets; 2.5 Something From Nothing; 2.6 Indexed Families of Sets; 3 Functions; 3.1 Functional Preliminaries; 3.2 Images and Preimages; 3.3 One-to-One and Onto Functions; 3.4 Bijections; 3.5 Inverse Functions; 4 Counting Infinite Sets…”