A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters. Written by an internationally recognized expert in the field of robust statistics, this book addresses a range of well-established techniques while exploring, in depth, new and exciting methodologies. Local robustness and global robustness are discussed, and problems of non-identifiability and adaptive estimation are considered. Rather than attempt an exhaustive investigation of robustness, the author provides readers with a timely review of many of the most important problems in statistical inference involving robust estimation, along with a brief look at confidence intervals for location. Throughout, the author meticulously links research in maximum likelihood estimation with the more general M-estimation methodology. Specific applications and R and some MATLAB subroutines with accompanying data sets--available both in the text and online--are employed wherever appropriate. Providing invaluable insights and guidance, Robustness Theory and Application Offers a balanced presentation of theory and applications within each topic-specific discussion Features solved examples throughout which help clarify complex and/or difficult concepts Meticulously links research in maximum likelihood type estimation with the more general M-estimation methodology Delves into new methodologies which have been developed over the past decade without stinting on coverage of "tried-and-true" methodologies Includes R and some MATLAB subroutines with accompanying data sets, which help illustrate the power of the methods described Robustness Theory and Application is an important resource for all statisticians interested in the topic of robust statistics. This book encompasses both past and present research, making it a valuable supplemental text for graduate-level courses in robustness.

This incisive book provides parents with the means to ensure that their deaf or hard of hearing child becomes a proficient reader and writer. In nine chapters, parents will learn about the relationship of language to reading and writing, including the associated terminology, the challenges that deaf children face, and the role of schools. They'll also learn activities that they can engage in at home that will strengthen their children's reading and writing capabilities. Literacy and Your Deaf Child outlines how children acquire language and describes the auditory and visual links to literacy. With this information parents can make informed decisions regarding hearing aids, cochlear implants, speechreading, and sign communication to best enhance their child's language development. Parents will discover how to create environments at home and in their community for fostering their child's literacy, especially in school by learning how to work closely with their child's teachers. The book also refers throughout to the developmental link between American Sign Language and English literacy for children who use sign communication, making it the best guide available for all deaf children and their parents.

An insightful approach to the analysis of variance in the study of linear models

Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from basic mathematical concepts in linear algebra.

The author begins with a presentation of the classic fixed-effects linear model and goes on to illustrate eight common linear models, along with the value of their use in statistics. From this foundation, subsequent chapters introduce concepts pertaining to the linear model, starting with vector space theory and the theory of least-squares estimation. An outline of the Helmert matrix is also presented, along with a thorough explanation of how the ANOVA is created in both typical two-way and higher layout designs, ultimately revealing the distribution theory. Other important topics covered include:

Vector space theory

The theory of least squares estimation

Gauss-Markov theorem

Kronecker products

Diagnostic and robust methods for linear models

Likelihood approaches to estimation

A discussion of Bayesian theory is also included for purposes of comparison and contrast, and numerous illustrative exercises assist the reader with uncovering the nature of the models, using both classic and new data sets. Requiring only a working knowledge of basic probability and statistical inference, Linear Models is a valuable book for courses on linear models at the upper-undergraduate and graduate levels. It is also an excellent reference for practitioners who use linear models to conduct research in the fields of econometrics, psychology, sociology, biology, and agriculture.