Variable selection in gamma regression models via artificial bee colony algorithm

Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.     

Time-consistent strategies for multi-period mean-variance portfolio optimization with the serially correlated returns

Abstract

In this paper, we discuss several different styles of multi-period mean-variance portfolio optimization problems under the serially correlated returns. We derive the time-consistent strategies for the classical multi-period mean-variance optimization with and without risk-free asset using a backward induction approach. We also propose an alternative multi-period mean-variance model, and the corresponding time-consistent strategies are derived. Whereafter, we provide some portfolio evaluation indexes and perform extensive empirical studies based on real data, aiming to provide useful advice for investors. To a large extent, the empirical results answer one important and practical question: in actual investment situations, which strategy is preferred by different investors?     

Bayesian optimization design for dose-finding based on toxicity and efficacy outcomes in phase I/II clinical trials

In phase I trials, the main goal is to identify a maximum tolerated dose under an assumption of monotonicity in dose–response relationships. On the other hand, such monotonicity is no longer applied to biologic agents because a different mode of action from that of cytotoxic agents potentially draws unimodal or flat dose–efficacy curves. Therefore, biologic agents require an optimal dose that provides a sufficient efficacy rate under an acceptable toxicity rate instead of a maximum tolerated dose. Many trials incorporate both toxicity and efficacy data, and drugs with a variety of modes of actions are increasingly being developed; thus, optimal dose estimation designs have been receiving increased attention. Although numerous authors have introduced parametric model-based designs, it is not always appropriate to apply strong assumptions in dose–response relationships. We propose a new design based on a Bayesian optimization framework for identifying optimal doses for biologic agents in phase I/II trials. Our proposed design models dose–response relationships via nonparametric models utilizing a Gaussian process prior, and the uncertainty of estimates is considered in the dose selection process. We compared the operating characteristics of our proposed design against those of three other designs through simulation studies. These include an expansion of Bayesian optimal interval design, the parametric model-based EffTox design, and the isotonic design. In simulations, our proposed design performed well and provided results that were more stable than those from the other designs, in terms of the accuracy of optimal dose estimations and the percentage of correct recommendations.     

基于煤矿透水事故应急响应时效性的水泵布局鲁棒优选问题研究

为了提高煤矿透水事故中矿工获救和恢复健康的可能性，本文基于应急响应时效性研究关键资源-水泵布局问题。已有应急资源布局问题的研究多从成本、时间、资源需求满足度等方面进行，煤矿透水事故应急响应时效性是综合考虑被困矿工获救时间以及被困过程中被困位置最低氧气含量和食物缺少量三方面要素的事故应对效果。首先，建立了基于煤矿透水事故发生、发展和应急响应机理的三方面要素计算方法；然后，在供氧和供食物所需资源布局给定的前提下，设计水泵布局的目标函数和约束条件，建立水泵布局鲁棒优选模型，通过机会时间窗概念的引入和分支定界算法的思想，给出水泵布局不可行方案的判别准则，并在此基础上进行优选算法的设计；最后，在实际发生的煤矿透水事故典型案例基础上，构造算例，检验水泵布局鲁棒优选模型的有效性。     

Nonlinear programming solutions to the nonstationary markov model

A nonstationary Markov process model with embedded explanatory variables offers a means to account for underlying causal factors while retaining unrestrictive assumptions and the predictive ability of a stochastic framework. We find that a direct search algorithm requiring minimal user preparation is a feasible computational procedure for estimating such a model. We compare this method with several others using factorially designed Monte Carlo simulations and find evidence that a small state space and a long time series lead to better algorithmic performance.     

A simulated annealing algorithm for system cost minimization subject to reliability constraints

A simulated annealing algorithm is presented that finds the minimum cost redundancy allocation subject to meeting a minimal reliability requirement for a coherent system of components. It is assumed that components are independent of one another, and that the form of the nominal system reliability function is available for input to the algorithm     

A neural network approach to response surface methodology

Response Surface Methodology is concerned with estimating a surface to a typically small set of observations with the purpose of determining what levels of the independent variables maximize the response. This usually entails fitting a quadratic regression function to the available data and calculating the function's derivatives.

Artificial Neural Networks are information-processing paradigms inspired by the way the human brain processes information. They are known to be universal function approximators under certain general conditions. This ability to approximate functions to any desired degree of accuracy makes them an attractive tool for use in a Response Surface analysis. This paper presents Artificial Neural Networks as a tool for Response Surface Methodology and demonstrates their use empirically.     

Formulas for the Exact LMS and LQS Estimators

The author presents the derivation of formulas for the calculation of critical values of the median function or the general version of it, namely, the quantile functions. In statistics, these functions are used to detect outliers in the data set and to make predictions that are resistant to outliers. Therefore, these formulas can also be used as estimators for these regressions. The fact that these formulas are able to calculate the global optimum gives the exact least median squares or the exact least quantile of squares estimators. The author provides the theoretical background for deriving these estimator formulas and derives the estimator formulas for regression models up to three parameters. In addition, the author provides guides for the derivation of formulas for other models, illustrates the use of these formulas, and emphasizes their properties that are useful for future works. One important conclusion is that each regression model has its own set of formulas.     

安徽省对外贸易商品结构的实证研究

伴随着我国出口贸易的迅速发展,我国出口商品结构和贸易发展方式发生了很大变化,从安徽省对外贸易商品结构的变化来看,已经向资本技术密集型进行转变,但优化幅度较小。初级产品出口处于劣势,依赖劳动密集型产品出口的传统优势没有根本改变;对资源密集型的非食品原料进口的依赖不断增加,而机械及运输设备的进口比重却在下降。今后,应该通过建设农业产业集群、巩固传统产品的出口、积极发展高新技术产品出口、促进资源类产品进口的多元化,以及加大对高科技含量产品的进口来优化安徽省对外贸易商品结构。     

海南野生灵芝培养基配方优化

以琼州学院栽培灵芝培养基配方为基础,通过对海南尖峰岭的黄边灵芝(Ganoderma luteomarhi-natum)和咖啡网孢芝(Humphreya coffeatum)培养基配方进行优化,并对实验结果进行比较分析,得出一级种最优培养基为PDA+1/4MS(马铃薯:葡萄糖:琼脂:MS培养基为10∶1∶1∶0.25);二级种的最优培养基配方为Y1005(木屑78%、麦麸或米糠20%、蔗糖1%、石膏1%、含水50-60%);三级种的最优培养基为Y3102(木屑73%、黄豆粉5%、麦麸20%、蔗糖1%、石膏1%、含水60-65%).