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Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions;

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Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions;

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Interval-Censored Time-to-Event Data

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for;

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Survival Analysis with Interval-Censored Data

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical;

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Survival Analysis with Interval-Censored Data

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical;

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Regression With Social Data

, and interval-censored survival models Concepts are reinforced throughout with numerous chapter problems, exercises, and real data sets. Step;

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Survival Analysis in Medicine and Genetics

Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional;

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Survival Analysis in Medicine and Genetics

Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional;

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Statistical Models and Methods for Reliability and Survival Analysis

interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non;

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Statistical Models and Methods for Lifetime Data

models Treatment of interval-censored and truncated data Multivariate lifetimes and event history models Resampling and simulation methodology;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Survival Analysis Using S

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;

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Mixed Effects Models for Complex Data

problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival;

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Mixed Effects Models for Complex Data

problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival;

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Statistical Modelling of Survival Data with Random Effects

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h;

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Analysis of Failure and Survival Data

Analysis of Failure and Survival Data is an essential textbook for graduate-level students of survival analysis and reliability and a;

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Analysis of Failure and Survival Data

Analysis of Failure and Survival Data is an essential textbook for graduate-level students of survival analysis and reliability and a;

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Nonparametric Tests for Censored Data

This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and;

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Regression Models

for which generalized results are preferred? Author Richard Breen provides an introduction to regression models for such data, including;

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Dynamic Prediction in Clinical Survival Analysis

background. The book consists of four parts: Part I deals with prognostic models for survival data using (clinical) information;

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Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications;

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Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Nonlinear Interval Optimization for Uncertain Problems

. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low;

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Counting Processes and Survival Analysis

methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications;

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