ABSTRACT

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.

chapter Chapter 1|20 pages

Odds Ratio Parameter and Its Utilities

chapter Chapter 2|34 pages

Odds Ratio Function and Its Modeling

chapter Chapter 5|28 pages

Application to Biased Sampling Problems

chapter Chapter 6|34 pages

Application to Test of Conditional Independence

chapter Chapter 7|22 pages

Application to Network Detection and Estimation

chapter Chapter 8|26 pages

Application to Missing Data Problems

chapter Chapter 9|14 pages

Other Applications

chapter Chapter 10|46 pages

Theoretical Results on Estimation and Inference