In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response s...In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response studies, a class of phenomena that frequently occur are that experimental subjects (e.g., mice) may have different responses like ’none, mild, severe’ after a toxicant experiment, or ’getting worse, no change, getting better’ after a medical treatment, etc. These phenomena have attracted the attention of many researchers in recent years. Finite展开更多
A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment group than others. Finite mixture models have traditionally been used to d...A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment group than others. Finite mixture models have traditionally been used to describe the distribution of responses in treated subjects for such studies. In this paper, we first study the mixture normal model with multi-levels and multiple mixture sub-populations under each level, with particular attention being given to the model in which the proportions of susceptibility are related to dose levels, then we use EM-algorithm to find the maximum likelihood estimators of model parameters. Our results are generalizations of the existing results. Finally, we illustrate realistic significance of the above extension based on a set of real dose-response data.展开更多
文摘In this paper, we consider the risk assessment problem under multi-levels and multiple mixture subpopulations. Our result is the generalization of the results of [1-5].1 Finite Mixture Normal ModelsIn dose-response studies, a class of phenomena that frequently occur are that experimental subjects (e.g., mice) may have different responses like ’none, mild, severe’ after a toxicant experiment, or ’getting worse, no change, getting better’ after a medical treatment, etc. These phenomena have attracted the attention of many researchers in recent years. Finite
基金Supported by the National Natural Science Foundation of China (No. 10571073)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070183023)Program for New Century Excellent Talents in University, Scientific Research Fund of Jilin University (No. 200810024)
文摘A problem that frequently occurs in biological experiments with laboratory animals is that some subjects are less susceptible to the treatment group than others. Finite mixture models have traditionally been used to describe the distribution of responses in treated subjects for such studies. In this paper, we first study the mixture normal model with multi-levels and multiple mixture sub-populations under each level, with particular attention being given to the model in which the proportions of susceptibility are related to dose levels, then we use EM-algorithm to find the maximum likelihood estimators of model parameters. Our results are generalizations of the existing results. Finally, we illustrate realistic significance of the above extension based on a set of real dose-response data.
