The case for function point analysis. (Technology Information): An article from: Soft-Letter

The case for function point analysis. (Technology Information): An article from: Soft-Letter Review

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The case for function point analysis. (Technology Information): An article from: Soft-Letter Feature

This digital document is an article from Soft-Letter, published by Soft-letter on March 29, 1996. The length of the article is 580 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.

Citation Details
Title: The case for function point analysis. (Technology Information)
Publication: Soft-Letter (Newsletter)
Date: March 29, 1996
Publisher: Soft-letter
Volume: v12 Issue: n15 Page: p4(2)

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Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health)

Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health) Review

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Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health) Feature

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

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