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Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.

Students will save time and master non-calculus-based probability and statistics with this powerful study guide. It simplifies difficult theories and focuses on making clear the areas students typically find hardest to understand. The hundreds of problems solved step-by-step make it easier to master even complex statistical problems and get the best grades. Ideal for students in liberal arts, social and health sciences, and education programs. Perfect to supplement class work or for independent study.

This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Some of the highlights include: – a special emphasis on sensitivity analysis and model selection; – a chapter devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models; – a chapter devoted to incomplete data sets; – an extensive appendix on matrix theory, useful to researchers in econometrics, engineering, and optimization theory; – a chapter devoted to the analysis of categorical data based on a unified presentation of generalized linear models including GEE- methods for correlated response; – a chapter devoted to incomplete data sets including regression diagnostics to identify Non-MCAR-processes The material covered will be invaluable not only to graduate students, but also to research workers and consultants in statistics. Helge Toutenburg is Professor for Statistics at the University of Muenchen. He has written about 15 books on linear models, statistical methods in quality engineering, and the analysis of designed experiments. His main interest is in the application of statistics to the fields of medicine and engineering.

“The second edition of this admirable book has grown by well over one hundred pages, including such new material as: multivariate and ratio ergodic theorems, shift coupling, Palm distributions, entropy and information, Harris recurrence, invariant measures, strong and weak ergodicity, Strassen’s LIL and the basic large deviation results. Also, a lot of existing material has been rewritten and expanded. I repeat a statement from my review of the first edition: “From the table of contents it is difficult to believe behind all these topics a streamlined readable text is at all possible. It is: Convince yourself.” Those who own the first edition should make some extra space for this second edition. Those who do not yet own a copy: buy one!”