A skew–normal mixture of joint location, scale and skewness models

Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.

基于相依数据的非参数回归函数和尺度函数的联立稳健估计

该文将Hrdle和Tsybakov的结果推广到数据来自α-混合的严平稳序列的情形,得到了估计的相合性和渐近正太性.在小样本的情形下给出了随机模拟结果,以检查所提出估计的表现.

粤西地区教育规模与农民收入的定量研究——以湛江市为例

在建设新型城镇化的背景下,粤西地区农村人口比重高,农民数量大,其生活改善与收入水平提高是该地区新型城镇化的重点。以教育规模为自变量,以农民收入为因变量,利用SPSS软件进行回归分析,建立函数模型。研究发现,小学与普通中学规模为排除变量,高等院校规模不完全具备建模条件,中职学校规模为主要因素,可作为教育规模的代表因素参与建模。结果显示,中职学校的规模与农民人均收入具有高度相关性。