Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We dis...Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We discuss potential applications of the distribution as a model of counts, and under the assumption of independence we will perform statistical inference on the ratio of two means, with generalization to testing the homogeneity of several means. Methods: Bayesian methods depend on the choice of the prior distributions of the population parameters. In this paper, we describe a Bayesian approach for estimation and inference on the parameters of several independent Inflated Poisson (IPD) distributions with two possible priors, the first is the reciprocal of the square root of the Poisson parameter and the other is a conjugate Gamma prior. The parameters of Gamma distribution are estimated in the empirical Bayesian framework using the maximum likelihood (ML) solution using nonlinear mixed model (NLMIXED) in SAS. With these priors we construct the highest posterior confidence intervals on the ratio of two IPD parameters and test the homogeneity of several populations. Results: We encountered convergence problem in estimating the hyperparameters of the posterior distribution using the NLMIXED. However, direct maximization of the predictive density produced solutions to the maximum likelihood equations. We apply the methodologies to RNA-SEQ read count data of gene expression values.展开更多
针对现有单类分类器对目标数据先验信息考虑的不足,在结构单类支持向量机(structured one-class supportvector machine,SOCSVM)中嵌入局部密度信息,提出局部密度嵌入的结构单类支持向量机(SOCSVM with local den-sity embedding ldSOCS...针对现有单类分类器对目标数据先验信息考虑的不足,在结构单类支持向量机(structured one-class supportvector machine,SOCSVM)中嵌入局部密度信息,提出局部密度嵌入的结构单类支持向量机(SOCSVM with local den-sity embedding ldSOCSVM)。借助K近邻(K-nearest neighbor,KNN)揭示目标数据局部密度,并进一步诱导出权重因子作用于样本点。该算法充分利用目标数据的全局信息及局部密度信息,从而提高分类器的泛化能力。UCI数据集上的实验结果验证了ldSOCSVM的有效性。展开更多
文摘Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We discuss potential applications of the distribution as a model of counts, and under the assumption of independence we will perform statistical inference on the ratio of two means, with generalization to testing the homogeneity of several means. Methods: Bayesian methods depend on the choice of the prior distributions of the population parameters. In this paper, we describe a Bayesian approach for estimation and inference on the parameters of several independent Inflated Poisson (IPD) distributions with two possible priors, the first is the reciprocal of the square root of the Poisson parameter and the other is a conjugate Gamma prior. The parameters of Gamma distribution are estimated in the empirical Bayesian framework using the maximum likelihood (ML) solution using nonlinear mixed model (NLMIXED) in SAS. With these priors we construct the highest posterior confidence intervals on the ratio of two IPD parameters and test the homogeneity of several populations. Results: We encountered convergence problem in estimating the hyperparameters of the posterior distribution using the NLMIXED. However, direct maximization of the predictive density produced solutions to the maximum likelihood equations. We apply the methodologies to RNA-SEQ read count data of gene expression values.
文摘针对现有单类分类器对目标数据先验信息考虑的不足,在结构单类支持向量机(structured one-class supportvector machine,SOCSVM)中嵌入局部密度信息,提出局部密度嵌入的结构单类支持向量机(SOCSVM with local den-sity embedding ldSOCSVM)。借助K近邻(K-nearest neighbor,KNN)揭示目标数据局部密度,并进一步诱导出权重因子作用于样本点。该算法充分利用目标数据的全局信息及局部密度信息,从而提高分类器的泛化能力。UCI数据集上的实验结果验证了ldSOCSVM的有效性。
