Description
Semiparametric Odds Ratio Model and Its Applications
Language: English
Subject for Semiparametric Odds Ratio Model and Its Applications:
Keywords
Odds Ratio Functions; semiparametirc models; Odds Ratio Parameters; missing data; Maximum Semiparametric Likelihood Estimator; survival data; Conditional Probability Distribution; Gibbs sampler; Gaussian Network; density estimation; Conditional Densities; Node Variables; Gaussian Network Model; Common Odds Ratio; Joint Model; Conditional Models; Network Detection; Spectral Density Estimation; Joint Density; Retrospective Likelihood; Conditional Likelihood; Penalty Parameter Values; Maximum Likelihood Estimator; Density Ratio Models; Exchangeable Conditions; Prospective Likelihood; Contingency Table; Normal Linear Regression Model; Semiparametric Density; Conditional Likelihood Approach
Publication date: 01-2024
· 17.8x25.4 cm · Paperback
Publication date: 12-2021
· 17.8x25.4 cm · Hardback
Description
/li>Contents
/li>Biography
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Beginning with familiar models and moving onto advanced semiparametric modelling tools Semiparametric Odds Ratio Model and its Applications introduces readers to a new range of flexible statistical models and provides guidance on their application using real data examples. This books range of real-world examples and exploration of common statistical problems makes it an invaluable reference for research professionals and graduate students of biostatistics, statistics, and other quantitative fields.
Key Features:
- Introduces flexible statistical models that have yet to systematically introduced in course materials.
- Discusses applications of the proposed modelling framework in several important statistical problems, ranging from biased sampling designs and missing data, graphical models, survival analysis, Gibbs sampler and model compatibility, and density estimation.
- Includes real data examples to demonstrate the use of the proposed models, and estimation and inference tools.
1. Odds Ratio Parameter and its Utilities. 2. Odds Ratio Functions and Their Modeling. 3. Estimation and Inference on Semiparametric Odds Ratio Model. 4. Estimation and Inference on Conditional Odds Ratio Function. 5. Application to Biased Sampling Problems. 6. Application to Missing Data Problems. 7. Application to Graphical Models. 8 Application to Survival Data. 9. Other Miscellaneous Applications
Dr. Hua Yun Chen received his PhD in Biostatistics from the University of Michigan. He is currently a Professor of Biostatistics at the University of Illinois at Chicago. His research focuses on statistical methods for incompletely observed data, biased sampling, and epidemiological applications.
