Skip to Main Content
 

Global Search Box

 
 
 
 

ETD Abstract Container

Abstract Header

Statistical Inferences under a semiparametric finite mixture model

Abstract Details

2005, Doctor of Philosophy, University of Toledo, Mathematics (statistics).
We consider the inference problem of a finite mixture model based on data from multiple samples, each of which is from a mixture of two common components. Under the assumption that the ratio of the two component densities takes a known parametric form, we obtain maximum semiparametric likelihood estimates of the parameters via EM or MM algorithms, and establish the large sample results for those estimators. We then develop empirical likelihood ratio-based statistics for constructing confidence intervals for and testing statistical hypotheses on mixing proportions. We show that the statistics are asymptotically chi-square distributed. In addition, a goodness-of-fit test is proposed for testing the density ratio assumption. Simulation studies are carried out to evaluate the performances of the proposed statistics and tests.
Biao Zhang (Advisor)
105 p.

Recommended Citations

Citations

  • Zhang, S. (2005). Statistical Inferences under a semiparametric finite mixture model [Doctoral dissertation, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1135779503

    APA Style (7th edition)

  • Zhang, Shiju. Statistical Inferences under a semiparametric finite mixture model. 2005. University of Toledo, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1135779503.

    MLA Style (8th edition)

  • Zhang, Shiju. "Statistical Inferences under a semiparametric finite mixture model." Doctoral dissertation, University of Toledo, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1135779503

    Chicago Manual of Style (17th edition)