A Bayesian multiple structural change regression model with autocorrelated errors

This paper develops a new Bayesian approach to change-point modeling that allows the number of change-points in the observed autocorrelated times series to be unknown. The model we develop assumes that the number of change-points have a truncated Poisson distribution. A genetic algorithm is used to estimate a change-point model, which allows for structural changes with autocorrelated errors. We focus considerable attention on the construction of autocorrelated structure for each regime and for the parameters that characterize each regime. Our techniques are found to work well in the simulation with a few change-points. An empirical analysis is provided involving the annual flow of the Nile River and the monthly total energy production in South Korea to lead good estimates for structural change-points.     

A Framework for Monte Carlo based Multiple Testing

We are concerned with a situation in which we would like to test multiple hypotheses with tests whose p‐values cannot be computed explicitly but can be approximated using Monte Carlo simulation. This scenario occurs widely in practice. We are interested in obtaining the same rejections and non‐rejections as the ones obtained if the p‐values for all hypotheses had been available. The present article introduces a framework for this scenario by providing a generic algorithm for a general multiple testing procedure. We establish conditions that guarantee that the rejections and non‐rejections obtained through Monte Carlo simulations are identical to the ones obtained with the p‐values. Our framework is applicable to a general class of step‐up and step‐down procedures, which includes many established multiple testing corrections such as the ones of Bonferroni, Holm, Sidak, Hochberg or Benjamini–Hochberg. Moreover, we show how to use our framework to improve algorithms available in the literature in such a way as to yield theoretical guarantees on their results. These modifications can easily be implemented in practice and lead to a particular way of reporting multiple testing results as three sets together with an error bound on their correctness, demonstrated exemplarily using a real biological dataset.     

A Kernel Smooth Approach for Joint Modeling of Accelerated Failure Time and Longitudinal Data

Joint likelihood approaches have been widely used to handle survival data with time-dependent covariates. In construction of the joint likelihood function for the accelerated failure time (AFT) model, the unspecified baseline hazard function is assumed to be a piecewise constant function in the literature. However, there are usually no close form formulas for the regression parameters, which require numerical methods in the EM iterations. The nonsmooth step function assumption leads to very spiky likelihood function which is very hard to find the globe maximum. Besides, due to nonsmoothness of the likelihood function, direct search methods are conducted for the maximization which are very inefficient and time consuming. To overcome the two disadvantages, we propose a kernel smooth pseudo-likelihood function to replace the nonsmooth step function assumption. The performance of the proposed method is evaluated by simulation studies. A case study of reproductive egg-laying data is provided to demonstrate the usefulness of the new approach.     

利用统计语言模型对GenoCAD设计结果进行优化

GenoCAD(www.genocad.com)是一种基于Web的免费合成生物学设计软件,使用它可以进行表达载体及人工基因网络设计。不断地点击代表各种合成生物学标准"零件"的图标,以一种语法进行设计,最后就可以得到由数十个功能片段组成的复杂质粒载体。但是一般来讲在GenoCAD中,每一类的合成生物学标准"零件"数量众多。随着这些标准"零件"的不断开发,其数量也在进一步增加,目前选择合适的"零件"组装成功能性的质粒载体费时费力并且容易发生错误。在进行载体设计的最后阶段,从众多的"零件"中选择合适的往往比较困难。为解决这一问题,采用自然语言处理的统计语言模型,并以该模型为基础应用动态规划算法优化质粒载体设计,从众多的选项中找出最优者。利用这一方法可以减少进行生物学实验的冗余操作,从而减少载体构建过程中的花费。     

Optimal decomposition of probabilistic networks by simulated annealing

This paper investigates the applicability of a Monte Carlo technique known as simulated annealing to achieve optimum or sub-optimum decompositions of probabilistic networks under bounded resources. High-quality decompositions are essential for performing efficient inference in probabilistic networks. Optimum decomposition of probabilistic networks is known to be NP-hard (Wen, 1990). The paper proves that cost-function changes can be computed locally, which is essential to the efficiency of the annealing algorithm. Pragmatic control schedules which reduce the running time of the annealing algorithm are presented and evaluated. Apart from the conventional temperature parameter, these schedules involve the radius of the search space as a new control parameter. The evaluation suggests that the inclusion of this new parameter is important for the success of the annealing algorithm for the present problem.     

基于连续混合正态分布的期货定价偏差研究

挖掘期货理论价格和实际价格之间的关系有助于提高期货市场定价效率、发挥期货价格发现功能。基于持有成本定价模型计算期货定价偏差,利用连续混合正态分布模型对定价偏差的分布进行拟合,先采用基于牛顿迭代的极大似然估计法对未知参数进行估计,再进一步利用模拟退火算法对牛顿迭代的结果进行优化。结果发现,模拟退火算法可以有效提高估计精度,连续混合正态分布模型能够更好地拟合期货定价偏差分布。     

A robust support vector machine for labeling errors

Support vector machine (SVM) is sparse in that its classifier is expressed as a linear combination of only a few support vectors (SVs). Whenever an outlier is included as an SV in the classifier, the outlier may have serious impact on the estimated decision function. In this article, we propose a robust loss function that is convex. Our learning algorithm is more robust to outliers than SVM. Also the convexity of our loss function permits an efficient solution path algorithm. Through simulated and real data analysis, we illustrate that our method can be useful in the presence of labeling errors.     

Mean and cell-mean imputation resulting in the use of a semicontinuous distribution and a mixture of semicontinuous distributions

Zero-inflated models are commonly used for modeling count and continuous data with extra zeros. Inflations at one point or two points apart from zero for modeling continuous data have been discussed less than that of zero inflation. In this article, inflation at an arbitrary point α as a semicontinuous distribution is presented and the mean imputation for a continuous response is discussed as a cause of having semicontinuous data. Also, inflation at two points and generally at k arbitrary points and their relation to cell-mean imputation in the mixture of continuous distributions are studied. To analyze the imputed data, a mixture of semicontinuous distributions is used. The effects of covariates on the dependent variable in a mixture of k semicontinuous distributions with inflation at k points are also investigated. In order to find the parameter estimates, the method of expectation–maximization (EM) algorithm is used. In a real data of Iranian Households Income and Expenditure Survey (IHIES), it is shown how to obtain a proper estimate of the population variance when continuous missing at random responses are mean imputed.     

大样本线性回归模型的子抽样及变量选择

文章考虑了大样本下线性回归中同时进行快速估计和变量选择的问题,即针对一个存在稀疏解的大样本线性模型,根据重要性抽样分布从全数据集抽取少量子样本,对该子样本进行自适应Lasso估计。通过随机模拟研究,将该算法分别应用在几种不同的数据集中,并从模型预测精度和可解释性两个方面比较了四种子抽样方法在该算法下的表现。模拟结果表明,所提出的算法具有良好表现,在计算开销上也具有一定优势。