Parametric Methods for Multiple Equation LDV Models

2008 ◽  
pp. 229-301
Author(s):  
Myoung-jae Lee
Keyword(s):  
Parametric Methods

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

Intrinsic vulnerability assessment of Saturnia thermal aquifer by means of three parametric methods: SINTACS, GODS and COP

2014 ◽  
Vol 72 (8) ◽  
pp. 2861-2878 ◽  
Author(s):  
Enrico Guastaldi ◽  
Luca Graziano ◽  
Giovanni Liali ◽  
Fabio Nunzio Antonio Brogna ◽  
Alessio Barbagli
Keyword(s):  
Vulnerability Assessment ◽  
Parametric Methods ◽  
Intrinsic Vulnerability

scholarly journals Nonparametric Density and Regression Estimation

2001 ◽  
Vol 15 (4) ◽  
pp. 11-28 ◽  
Author(s):  
John DiNardo ◽  
Justin L Tobias
Keyword(s):  
Functional Form ◽  
Regression Function ◽  
Nonparametric Methods ◽  
Actual Data ◽  
Attractive Alternative ◽  
Regression Estimation ◽  
Parametric Methods ◽  
Regression Functions

We provide a nontechnical review of recent nonparametric methods for estimating density and regression functions. The methods we describe make it possible for a researcher to estimate a regression function or density without having to specify in advance a particular--and hence potentially misspecified functional form. We compare these methods to more popular parametric alternatives (such as OLS), illustrate their use in several applications, and demonstrate their flexibility with actual data and generated-data experiments. We show that these methods are intuitive and easily implemented, and in the appropriate context may provide an attractive alternative to “simpler” parametric methods.

Sign in / Sign up

Export Citation Format

Share Document