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Flowgraph Models For Multistate Time-To-Event Data

flowgraph models for time--to--event data. It clearly shows how this innovative new methodology can be used to analyze data from semi--Markov;

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Dynamical Biostatistical Models

measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and;

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Multistate Models for the Analysis of Life History Data

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis;

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Multistate Models for the Analysis of Life History Data

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis;

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Introducing Survival & Event Hist Analys

theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and;

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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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Joint Models of Longitudinal a

-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint;

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Regression With Social Data

response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;

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Techniques Of Event History Modeling

sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history;

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Techniques of Event History Modeling

sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history;

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Event History Analysis With Stata

statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the;

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Applied Survival Analysis

Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical;

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Event History Analysis with R

With an emphasis on social science applications, Event History Analysis with R, Second Edition, presents an introduction to survival and;

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Time Series Analysis for the Social Sciences

Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced;

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Event History Analysis

the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data to;

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Econometrics

spillovers; econometric modelling of time to event data; regression and data envelopment analysis methods to assess practice efficiency and;

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Event Mining for Explanatory Modeling

knowledge and judgment is used to test and modify the candidate hypotheses. The book is intended as a primer on Event Mining for data-enthusiasts;

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Event Mining for Explanatory Modeling

knowledge and judgment is used to test and modify the candidate hypotheses. The book is intended as a primer on Event Mining for data-enthusiasts;

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Survival and Event History Analysis

presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual;

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Multistate System Reliability with Dependencies

Multistate System Reliability with Dependencies explains how to select a model of load sharing that best describes the impact;

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Event History Analysis: Statistical Theory and Application in the Social Sciences

the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data to;

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App Panel Data Analysis For Economic & S

causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for;

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From Words to Numbers

, is the linguistic tool the author uses to structure narrative for the purpose of collecting event data. Relational database models make such;

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Structural Analysis in the Social Sciences

, is the linguistic tool the author uses to structure narrative for the purpose of collecting event data. Relational database models make such;

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Advanced Survival Models

experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event;

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Advanced Survival Models

experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event;

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Statistics for Environmental Biology and Toxicology

progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and;

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