A multivariate zero-inflated binomial model for the analysis of correlated proportional data

In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.     

求解非线性二层规划问题的模拟植物生长算法

本文以植物向光性生长理论为启发式准则,提出了一种求解非线性二层规划问题的智能优化算法。在该算法中,将二层规划上层解空间和下层反应集分别作为植物的两个生长环境,建立以生长规则为基础的植物系统演绎方式和以植物向光性理论为基础的概率生长模型,两者结合所形成的优化模式,实现了模拟植物从初始状态到完整形式的终态(没有新的树枝生长),从而得到二层规划问题的解。该方法具有搜索精度较高,求解稳定性较强的特点,通过与国外学者在非线性二层规划实际测试问题的最优值进行精度比较,表明模拟植物生长算法是有效可行的。     

一维优化的最优步长因子法

本文在分析一维优化方法的比例因子法的基础上,提出了一种直接调整步长的最优步长因子法并给出框图。该法较比例因子法理论上完善,迭代控制可靠。     

时频混合重叠复用系统的快速检测算法

为提高通信系统的频谱效率,研究了一种时频混合重叠复用系统,并提出针对该系统的基于Gabor变换的快速检测算法和利用误差反向传播思想的改进检测算法。实验结果表明,与利用最大似然序列估计检测算法相比,该算法在牺牲部分性能的条件下,检测速度大幅提高,且系统性能和检测速度做了很好的折衷,在较高信噪比时仍然优于相同频谱效率的高阶调制。采用相互重叠的信号复用传输方式直接提高传输速率比使用同样频谱效率的高阶QAM调制能带来更大的增益。     

基于WiMAX的调度算法综述

随着无线网络的发展,网络服务质量（Qos）也越来越成为用户和运营商共同关心的问题。保障Qos的问题实质上是一个分配和调度用户间资源的问题。调度算法的目的在于最优化系统的资源分配从而保障Qos,最大化系统的吞吐量,最小化系统能量消耗。本文简要综述在信道状态已知的情况下的调度算法。     

Fitting competing risks data to bivariate Pareto models

This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model introduced by Sankaran and Nair (1993 Sankaran, P. G., and N. U. Nair. 1993. A bivariate Pareto model and its applications to reliability. Naval Research Logistics 40 (7):1013–1020. doi:10.1002/1520-6750(199312)40:7%3c1013::AID-NAV3220400711%3e3.0.CO;2-7.[Crossref], [Web of Science ®] , [Google Scholar]). We discuss the identifiability issues of these models and develop the maximum likelihood estimation procedures including their computational algorithms and model-diagnostic procedures. Simulations are conducted to examine the performance of the maximum likelihood estimation. Real data are analyzed for illustration.     

Implementing Monte Carlo tests with p-value buckets

Software packages usually report the results of statistical tests using p-values. Users often interpret these values by comparing them with standard thresholds, for example, 0.1, 1, and 5%, which is sometimes reinforced by a star rating (***, **, and *, respectively). We consider an arbitrary statistical test whose p-value p is not available explicitly, but can be approximated by Monte Carlo samples, for example, by bootstrap or permutation tests. The standard implementation of such tests usually draws a fixed number of samples to approximate p. However, the probability that the exact and the approximated p-value lie on different sides of a threshold (the resampling risk) can be high, particularly for p-values close to a threshold. We present a method to overcome this. We consider a finite set of user-specified intervals that cover [0, 1] and that can be overlapping. We call these p-value buckets. We present algorithms that, with arbitrarily high probability, return a p-value bucket containing p. We prove that for both a bounded resampling risk and a finite runtime, overlapping buckets need to be employed, and that our methods both bound the resampling risk and guarantee a finite runtime for such overlapping buckets. To interpret decisions with overlapping buckets, we propose an extension of the star rating system. We demonstrate that our methods are suitable for use in standard software, including for low p-value thresholds occurring in multiple testing settings, and that they can be computationally more efficient than standard implementations.     

Penalized proportion estimation for non parametric mixture of regressions

Abstract

In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.     

基于决策树算法的农业上市公司股利分配政策研究

股利分配政策是上市公司对盈利进行分配或留存的决策问题.在大数据时代运用微软商业智能中的决策树算法,可以对农业上市公司的相关财务数据进行深入挖掘.通过决策树规则进行分析,找出农业上市公司股利分配采购影响因素,为农业上市公司制定现金股利分配股利分配政策提供决策依据,也为商业智能工具在企业决策中的运用提供借鉴.     

一种应用于药品泡罩包装检测的改进分水岭算法研究

针对分水岭算法存在的过分割问题以及泡罩包装药品图像的特点,提出一种改进的分水岭分割算法对泡罩包装的药品进行分割。该方法将形态学重建运算与使用控制标记符的方法相结合,首先对原图像进行开闭重建,根据邻域像素连通性提取重建后图像的局部极大值作为标记,从而抑制其他的局部极小值,然后依据标记对原始的梯度图像进行修正,最后对修改后的梯度图像进行分水岭分割。试验表明,该方法在缺陷药品检测中,可以有效的抑制传统分水岭的过分割,分割效果良好。