Order-restricted semiparametric inference for the power bias model
Date
2010ISSN
0006-341XSource
BiometricsVolume
66Issue
2Pages
549-557Google Scholar check
Keyword(s):
Metadata
Show full item recordAbstract
The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach. © 2009, The International Biometric Society.
Collections
Cite as
Related items
Showing items related by title, author, creator and subject.
-
Article
Energy-based model reduction methodology for automated modeling
Louca, Loucas S.; Stein, J. L.; Hulbert, G. M. (2010)In recent years, algorithms have been developed to help automate the production of dynamic system models. Part of this effort has been the development of algorithms that use modeling metrics for generating minimum complexity ...
-
Article
A review of proper modeling techniques
Ersal, T.; Fathy, H. K.; Rideout, D. G.; Louca, Loucas S.; Stein, J. L. (2008)A dynamic system model is proper for a particular application if it achieves the accuracy required by the application with minimal complexity. Because model complexity often-but not always-correlates inversely with simulation ...
-
Conference Object
A model accuracy and validation algorithm
Sendur, P.; Stein, J. L.; Peng, H.; Louca, Loucas S. (American Society of Mechanical Engineers (ASME), 2002)Dynamic models of physical systems with physically meaningful states and parameters have become increasingly important, for design, control and even procurement decisions. The successful use of models in these contexts ...