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Survival Analysis Using S: Analysis of Time-to-Event Data (Chapman & Hall/CRC Texts in Statistical Science)

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Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics.

The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s).

In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
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Algorithms for Sparsity-Constrained Optimization (Springer Theses)
Algorithms for Sparsity-Constrained Optimization (Springer Theses)
This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use...
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition
Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and...

Repairing and Querying Databases under Aggregate Constraints (SpringerBriefs in Computer Science)
Repairing and Querying Databases under Aggregate Constraints (SpringerBriefs in Computer Science)
Research has deeply investigated several issues related to the use of integrity constraints on relational databases. In particular, a great deal of attention has been devoted to the problem of extracting "reliable" information from databases containing pieces of information inconsistent with regard to some integrity constraints. In...

SQL Success - Database Programming Proficiency
SQL Success - Database Programming Proficiency
SQL Success is about problem-solving in SQL. It bridges the gap between dry and dull database theory books, and developer books that focus on giving recipes without explaining sufficiently the reasons behind the recipes or discussing alternative solutions.
Many developers struggle with SQL due to the contrast between
...
Mathematics of Discrete Structures for Computer Science
Mathematics of Discrete Structures for Computer Science

Mathematics plays a key role in computer science, some researchers would consider computers as nothing but the physical embodiment of mathematical systems. And whether you are designing a digital circuit, a computer program or a new programming language, you need mathematics to be able to reason about the design -- its correctness,...

Optimization Algorithms for Networks and Graphs
Optimization Algorithms for Networks and Graphs
A revised and expanded advanced-undergraduate/graduate text (first ed., 1978) about optimization algorithms for problems that can be formulated on graphs and networks. This edition provides many new applications and algorithms while maintaining the classic foundations on which contemporary algorithm...
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