Books like Nonlinear regression by G. A. F. Seber

A concise, one‑semester text that systematically develops the theory and application of linear models, covering regression, matrix fundamentals, analysis of variance, and statistical inference.

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

A practical guide to regression and linear modeling that covers ordinary least squares, logistic, ordinal, Poisson, and loglinear techniques with a focus on assumptions, data quality, and nonlinear extensions.

"An Introduction to Generalized Linear Models, Second Edition initiates intermediate students of statistics, and the many other disciplines that use statistics, in the practical use of these models and methods. The new edition incorporates many of the important developments of the last decade, including those in survival analysis, nominal and ordinal logistic regression, generalized estimating equations, and multi-level models. It also includes modern methods for checking model adequacy.". "The text assumes a working knowledge of basic statistical concepts and methods and an acquaintance with calculus and matrix algebra. It emphasizes graphical methods for exploratory data analysis, visualizing numerical optimization, and plotting residuals, and now includes examples from a wider range of application areas, including business, medicine, agriculture, biology, engineering, and the social sciences. Data sets and outline solutions to exercises are available on the internet."--BOOK JACKET.

A Beginner's Guide to GAMM with R is the third in Highland Statistics' Beginner's Guide series, following the well-received A Beginner's Guide to Generalized Additive Models with R and A Beginner's Guide to GLM and GLMM with R. In this book we take the reader on an exciting voyage into the world of generalized additive mixed effects models (GAMM). Keywords are GAM, mgcv, gamm4, random effects, Poisson and negative binomial GAMM, gamma GAMM, binomial GAMM, NB-P models, GAMMs with generalized extreme value distributions, overdispersion, underdispersion, two-dimensional smoothers, zero-inflated GAMMs, spatial correlation, INLA, Markov chain Monte Carlo techniques, JAGS, and two-way nested GAMMs. The book includes three chapters on the analysis of zero-inflated data. Across the book frequentist approaches (gam, gamm, gamm4, lme4) are compared with Bayesian techniques (MCMC in JAGS and INLA). Datasets on squid, polar bears, coral reefs, ruddy turnstones, parasites in anchovy, common guillemots, harbor porpoises, forestry, brood parasitism, maximum cod length, and Common Scoters are used in case studies. The R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage.