Rao R.C., Toutenburg H. Linear Models: Least Squares and Alternatives

Oxford University Press, 1974. - 286 pages. This is the classic book of detailed instructions for making a wide variety of mathematical models of all kinds Complete nets are given for all regular Archimedean and stellated polyhedra together with a number of interesting compounds. There are sections on paper folding, dissections, curve stitching, linkages, the drawing of loci and envelopes and the construction of plane tessellations. The volume i...

Society for Industrial Applied Mathematics, 1987. - 402 pages. Mathematics is a grand subject in the way it can be applied to various problems in science and engineering. To use mathematics, one needs to understand the physical context. The author uses mathematical techniques along with observations and experiments to give an in-depth look at models for mechanical vibrations, population dynamics, and traffic flow. Equal emphasis is placed on the...

Springer, 2005. - 164 Pages. Proposing a wide range of mathematical models that are currently used in life sciences may be regarded as a challenge, and that is precisely the challenge that this book takes up. Of course this panoramic study does not claim to offer a detailed and exhaustive view of the many interactions between mathematical models and life sciences. This textbook provides a general overview of realistic mathematical models in life...

Springer, 2011. 288 p. ISBN: 9400704488 The process of developing models, known as modeling, allows scientists to visualize difficult concepts, explain complex phenomena and clarify intricate theories. In recent years, science educators have greatly increased their use of modeling in teaching, especially real-time dynamic modeling, which is central to a scientific investigation. Modeling in science teaching is being used in an array of fields,...

North Holland, 2007. - 458 Pages. This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine,...

Wiley-Interscience, 2001. - 348 pages. The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's clas...

Wiley-Interscience, 2001. - 348 pages. The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classi...

Paper - 2003 The majority of computational methods applied for the analysis of homogeneous Markov reward models (MRMs) are not applicable for the analysis of inhomogeneous MRMs. By the nature of inhomogeneous models, only forward differential equations can be used to describe the model behaviour. In this paper we provide forward partial differential equations describing the distribution of reward measures of inhomogeneous MRMs. Based on this des...

Wiley, 2009. - 362 pages. This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra — all other concepts and ideas are developed in the course of the book. Lucidly written so as to appeal to undergraduates and practitioners alike, it enables readers to set up simple mathematical models on their own and to interpret their results and those of others critically. To achieve this, many examples hav...

Cambridge University Press, 2006. - 216 Pages. This book is about the coordinate-free, or geometric, approach to the theory of linear models; more precisely, Model I ANOVA and linear regression models with nonrandom predictors in a finite-dimensional setting. This approach is more insightful, more elegant, more direct, and simpler than the more common matrix approach to linear regression, analysis of variance, and analysis of covariance models i...