Найти книгу: "Goodness"

This remarkable autobiographical play by the award-winning author of Building Jerusalem and Martin Sloane, is a Russian-doll-like play: concentric stories enveloping each other. A writer is told, in confidence, a terrible tale of murder and injustice and he promises never to repeat the story. Goodness is the writer breaking his word. Recently divorced, Michael Redhill goes to Poland to get away frm his life and to do some research on the Holocaust. Thwarted by witnesses unwilling to talk, he returns home via England, but in London is introduced to someone who can tell him a 'real' story of evil. Through this reluctant witness, Redhill learns of a genocide. He encounters, through the memory of the storyteller, an alleged war criminal, about to be put on trial. But this is an old man with Alzheimer's who can no longer remember the time his crimes were allegedly committed. Has his guilt dissolved with his memory? Could he be pretending to be ill in order to escape punishment? The witness conjures for Redhill the war criminal's passionate and beautiful daughter, who will defend her father at all costs. There is also the prosecuting attorney, who has much in common with the old man whose destruction he seeks. As well as an uncomfortable attraction to his daughter. Each is drawn to the other. All is witnessed by a female prison guard – the one who tells the playwright, years later, what really happened in the quest to give a nation some closure. Everyone's story is compelling, and the ending is as unexpected as it is shocking. Who do we believe? A prison guard still wounded by history? A writer suffering from heartache? A dying war criminal? What is our responsibility? Who does memory serve? Did the past really happen? And if it did, who has a claim on it? Goodness is a play about what happens in the gaps between experiencing, telling and hearing.

This book is devoted to the problems of construction and application of chi-squared goodness-of-fit tests for complete and censored data. Classical chi-squared tests assume that unknown distribution parameters are estimated using grouped data, but in practice this assumption is often forgotten. In this book, we consider modified chi-squared tests, which do not suffer from such a drawback. The authors provide examples of chi-squared tests for various distributions widely used in practice, and also consider chi-squared tests for the parametric proportional hazards model and accelerated failure time model, which are widely used in reliability and survival analysis. Particular attention is paid to the choice of grouping intervals and simulations. This book covers recent innovations in the field as well as important results previously only published in Russian. Chi-squared tests are compared with other goodness-of-fit tests (such as the Cramer-von Mises-Smirnov, Anderson-Darling and Zhang tests) in terms of power when testing close competing hypotheses.

In this fully revised and expanded edition of Smooth Tests of Goodness of Fit, the latest powerful techniques for assessing statistical and probabilistic models using this proven class of procedures are presented in a practical and easily accessible manner. Emphasis is placed on modern developments such as data-driven tests, diagnostic properties, and model selection techniques. Applicable to most statistical distributions, the methodology described in this book is optimal for deriving tests of fit for new distributions and complex probabilistic models, and is a standard against which new procedures should be compared. New features of the second edition include: Expansion of the methodology to cover virtually any statistical distribution, including exponential families Discussion and application of data-driven smooth tests Techniques for the selection of the best model for the data, with a guide to acceptable alternatives Numerous new, revised, and expanded examples, generated using R code Smooth Tests of Goodness of Fit is an invaluable resource for all methodological researchers as well as graduate students undertaking goodness-of-fit, statistical, and probabilistic model assessment courses. Practitioners wishing to make an informed choice of goodness-of-fit test will also find this book an indispensible guide. Reviews of the first edition: «This book gives a very readable account of the smooth tests of goodness of fit. The book can be read by scientists having only an introductory knowledge of statistics. It contains a fairly extensive list of references; research will find it helpful for the further development of smooth tests.» –T.K. Chandra, Zentralblatt fur Mathematik und ihre Grenzgebiete, Band 73, 1/92' «An excellent job of showing how smooth tests (a class of goodness of fit tests) are generally and easily applicable in assessing the validity of models involving statistical distributions....Highly recommended for undergraduate and graduate libraries.» –Choice «The book can be read by scientists having only an introductory knowledge of statistics. It contains a fairly extensive list of references; researchers will find it helpful for the further development of smooth tests.»–Mathematical Reviews «Very rich in examples . . . Should find its way to the desks of many statisticians.» –Technometrics