Editorial

This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.     

Methods for estimating proportions of convex combinations of normals using linear feature selection

Let X be a random n-vector whose density function is given by a mixtur.e of two density functions, h₁ and h₂ with unknown mixture proportions, Y₁ and Y₂ We assume that each of h₁ and h₂ is a convex combination of known multivariate normal density functions whose corresponding mixture proportions are also unknown. We present three numerically tractable methods for estimating Y₁ and Y₂ related to the technique of Guseman and Walton (1977), and based on the linear feature selection technique of Guseman, Peters and Walker (1975).     

Constrained nonparametric maximum likelihood estimation of stochastically ordered survivor functions

This paper considers estimators of survivor functions subject to a stochastic ordering constraint based on right censored data. We present the constrained nonparametric maximum likelihood estimator (C‐NPMLE) of the survivor functions in one‐and two‐sample settings where the survivor distributions could be discrete or continuous and discuss the non‐uniqueness of the estimators. We also present a computationally efficient algorithm to obtain the C‐NPMLE. To address the possibility of non‐uniqueness of the C‐NPMLE of $S_1(t)$ when $S_1(t)\le S_2(t)$ , we consider the maximum C‐NPMLE (MC‐NPMLE) of $S_1(t)$ . In the one‐sample case with arbitrary upper bound survivor function $S_2(t)$ , we present a novel and efficient algorithm for finding the MC‐NPMLE of $S_1(t)$ . Dykstra ( 1982 ) also considered constrained nonparametric maximum likelihood estimation for such problems, however, as we show, Dykstra's method has an error and does not always give the C‐NPMLE. We corrected this error and simulation shows improvement in efficiency compared to Dykstra's estimator. Confidence intervals based on bootstrap methods are proposed and consistency of the estimators is proved. Data from a study on larynx cancer are analysed to illustrate the method. The Canadian Journal of Statistics 40: 22–39; 2012 © 2012 Statistical Society of Canada     

多油品供给受限下多油库被动配送车辆路径问题研究

成品油供给不足将导致加油站油品订单无法完全满足,如何安排有限油品的合理配送对保障能源供给安全至关重要。为此,本文考虑有限供给下不同客户配送的优先次序,开展配送计划、车辆调度和路径优化等油品配送网络规划活动,对多油品供给受限情况下多油库被动配送车辆路径问题（Multiple Depot Vehicle Routing Problem,MDVRP）进行深入研究。首先,文章构建了考虑需求优先等级和配送成本的多油品多油库车辆路径规划多目标优化模型。其次,采用多目标粒子群优化算法（Multi-Objective Particle Swarm Optimization,MOPSO）对模型进行求解,以实现车辆高效调度和油品配送路径优化。最后,基于CNPC在青岛市部分油库和加油站点的数据信息,构建油品配送网络进行实证检验。算例结果显示,配送车辆路径经过优化后,生成Pareto非劣解集,配送成本显著降低,配送满足率明显提高,这也进一步验证了该模型及相关算法的可行性和有效性。     

广东农村成人教育发展研究

要增强农村的发展后劲,必须正视和提高人力资本存量,最快捷的办法是发展农村成人教育;广东农村成人教育发展现状喜忧参半,不能适应广东农村经济发展的要求。在分析制约广东农村成人教育发展因素的基础上,提出更新观念、城乡统等、创新教育的发展措施。     

Chance constrained optimization for targeted Internet advertising

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation.     

Novel concentration-QTc models for early clinical studies with parallel placebo controls: A simulation study

The QTc interval of the electrocardiogram is a pharmacodynamic biomarker for drug-induced cardiac toxicity. The ICH E14 guideline Questions and Answers offer a solution for evaluating a concentration-QTc relationship in early clinical studies as an alternative to conducting a thorough QT/QTc study. We focused on covariance structures of QTc intervals on the baseline day and dosing day (two-day covariance structure,) and proposed a two-day QTc model to analyze a concentration-QTc relationship for placebo-controlled parallel phase 1 single ascending dose studies. The proposed two-day QTc model is based on a constrained longitudinal data analysis model and a mixed effects model, thus allowing various variance components to capture the two-day covariance structure. We also propose a one-day QTc model for the situation where no baseline day or only a pre-dose baseline is available and models for multiple ascending dose studies where concentration and QTc intervals are available over multiple days. A simulation study shows that the proposed models control the false negative rate for positive drugs and have both higher accuracy and power for negative drugs than existing models in a variety of settings for the two-day covariance structure. The proposed models will promote early and accurate evaluation of the cardiac safety of new drugs.     

An easy way to tell what you are esting in analysis of variance

Least squares regression models are often used to analyze unbalanced fixed effect data sets with u unique cells defined by design or by post-hoc stratification. Constraints exist among the regression coefficients if there are more coefficients than cells. Models with fewer linearly independent regression coefficients than cells or with empty cells impose constraints on estimated cell means. An easy method of determining constraints among the estimated cell means and among the estimated regression coefficients for any model is developed and illustrated using a small data set.     

Sensitivity analysis in ordered and restricted parameter models

This paper deals with the problem of local sensitivity analysis in ordered parameter models. In addition to order restrictions, some constraints imposed on the parameters by the model and/or the data are considered. Measures for assessing how much a change in the data modifies the results and conclusions of a statistical analysis of these models are presented. The sensitivity measures are derived using recent results in mathematical programming. The estimation problem is formulated as a primal nonlinear programming problem, and the sensitivities of the parameter estimates as well as the objective function sensitivities with respect to data are obtained. They are very effective in revealing the influential observations in this type of models and in evaluating the changes due to changes in data values. The methods are illustrated by their application to a wide variety of examples of order-restricted models including ordered exponential family parameters, ordered multinomial parameters, ordered linear model parameters, ordered and data constrained parameters, and ordered functions of parameters.     

A descent algorithm for constrained LAD-Lasso estimation with applications in portfolio selection

To improve the out-of-sample performance of the portfolio, Lasso regularization is incorporated to the Mean Absolute Deviance (MAD)-based portfolio selection method. It is shown that such a portfolio selection problem can be reformulated as a constrained Least Absolute Deviance problem with linear equality constraints. Moreover, we propose a new descent algorithm based on the ideas of ‘nonsmooth optimality conditions’ and ‘basis descent direction set’. The resulting MAD-Lasso method enjoys at least two advantages. First, it does not involve the estimation of covariance matrix that is difficult particularly in the high-dimensional settings. Second, sparsity is encouraged. This means that assets with weights close to zero in the Markovwitz's portfolio are driven to zero automatically. This reduces the management cost of the portfolio. Extensive simulation and real data examples indicate that if the Lasso regularization is incorporated, MAD portfolio selection method is consistently improved in terms of out-of-sample performance, measured by Sharpe ratio and sparsity. Moreover, simulation results suggest that the proposed descent algorithm is more time-efficient than interior point method and ADMM algorithm.