Nonconcave penalized likelihood with a diverging number of parameters

A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed by Fan and Li to simultaneously estimate parameters and select important variables. They demonstrated that this class of procedures has an oracle property when the number of parameters is finite. However, in most model selection problems the number of parameters should be large and grow with the sample size. In this paper some asymptotic properties of the nonconcave penalized likelihood are established for situations in which the number of parameters tends to \infty as the sample size increases. Under regularity conditions we have established an oracle property and the asymptotic normality of the penalized likelihood estimators. Furthermore, the consistency of the sandwich formula of the covariance matrix is demonstrated. Nonconcave penalized likelihood ratio statistics are discussed, and their asymptotic distributions under the null hypothesis are obtained by imposing some mild conditions on the penalty functions

Regional Fisheries Livelihoods Programme for South and Southeast Asia (RFLP) Activity 1.5 (2011): Systems and Procedures for Participatory Monitoring of Management Measures Developed, Introduced and Implemented-catch Monitoring

Coastal fisheries are very essential for supporting the livelihoods of many rural poor in the coastal areas, particularly coastal community fisheries members. They serve as sources of food, employment and income generation for many coastal families. "Coastal Community Fisheries Catch Monitoring" Project which was conducted from April to November 2011 provides some data which indicates the importance of small-scale fisheries. The project was financially supported by the Regional Fisheries Livelihoods Programme, Cambodian component (RFLP/CMB) and was activity 1.5 of the approved RFLP CMB 2011 activity work plan and budget. For this catch monitoring study, 26 small-scale subsistence fishers, including 05 women, from five community fisheries (CFi?s) in the RFLP CMB area of geographic coverage from the four coastal provinces of Cambodia were selected and following appropriate training collected specific catch data and recorded it in fisher's logbook on a daily basis for the purpose of getting a better understanding of catch per unit of effort (CEPU), the health of inshore fish stock and the contribution of aquatic products to small-scale fisher households along the coast of Cambodia. The key data items recorded included total catchweight, catch weight by species, total catch sale price, fish price of the main species and total lengths of some key species. The study involved designing logbooks, training the selected 26 fishers as data collectors on data collection methods, collecting data from all the selected fishers, designing a database and entering all the collected data into the database, checking for errors and analyzing the collected data for final reporting and preparing report

Model Selection for Gaussian Mixture Models

This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified EM algorithm is developed to simultaneously select the number of components and to estimate the mixing weights, i.e. the mixing probabilities, and unknown parameters of Gaussian distributions. Simulations and a real data analysis are presented to illustrate the performance of the proposed method