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Innovative Statistical Methods for Public Health Data

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Cover of 'Innovative Statistical Methods for Public Health Data'

Table of Contents

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    Book Overview
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    Chapter 1 Methods for Analyzing Secondary Outcomes in Public Health Case–Control Studies
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    Chapter 2 Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems
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    Chapter 3 On the Inference of Partially Correlated Data with Applications to Public Health Issues
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    Chapter 4 Modeling Time-Dependent Covariates in Longitudinal Data Analyses
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    Chapter 5 Solving Probabilistic Discrete Event Systems with Moore–Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data
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    Chapter 6 On the Effects of Structural Zeros in Regression Models
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    Chapter 7 Modeling Based on Progressively Type-I Interval Censored Sample
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    Chapter 8 Techniques for Analyzing Incomplete Data in Public Health Research
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    Chapter 9 A Continuous Latent Factor Model for Non-ignorable Missing Data
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    Chapter 10 Health Surveillance
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    Chapter 11 Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies
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    Chapter 12 Cusp Catastrophe Modeling in Medical and Health Research
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    Chapter 13 On Ranked Set Sampling Variation and Its Applications to Public Health Research
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    Chapter 14 Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data
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    Chapter 15 Meta-Analytic Methods for Public Health Research
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Title
Innovative Statistical Methods for Public Health Data
Published by
Springer International Publishing, January 2015
DOI 10.1007/978-3-319-18536-1
ISBNs
978-3-31-918535-4, 978-3-31-918536-1
Editors

Ding-Geng (Din) Chen, Jeffrey Wilson

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Student > Master 10 18%
Other 7 12%
Researcher 6 11%
Student > Doctoral Student 4 7%
Other 7 12%
Unknown 12 21%
Readers by discipline Count As %
Medicine and Dentistry 10 18%
Mathematics 7 12%
Agricultural and Biological Sciences 5 9%
Psychology 5 9%
Neuroscience 3 5%
Other 13 23%
Unknown 14 25%