A graphical evaluation of logistic ridge estimator in mixture experiments

In comparison to other experimental studies, multicollinearity appears frequently in mixture experiments, a special study area of response surface methodology, due to the constraints on the components composing the mixture. In the analysis of mixture experiments by using a special generalized linear model, logistic regression model, multicollinearity causes precision problems in the maximum-likelihood logistic regression estimate. Therefore, effects due to multicollinearity can be reduced to a certain extent by using alternative approaches. One of these approaches is to use biased estimators for the estimation of the coefficients. In this paper, we suggest the use of logistic ridge regression (RR) estimator in the cases where there is multicollinearity during the analysis of mixture experiments using logistic regression. Also, for the selection of the biasing parameter, we use fraction of design space plots for evaluating the effect of the logistic RR estimator with respect to the scaled mean squared error of prediction. The suggested graphical approaches are illustrated on the tumor incidence data set.     

Prediction of Times to Failure of Censored Items for a Simple Step-Stress Model with Regular and Progressive Type I Censoring from the Exponential Distribution

The problem of predicting times to failure of units from the Exponential Distribution which are censored under a simple step-stress model is considered in this article. We discuss two types of censoring—regular and progressive Type I—and two kinds of predictors—the maximum likelihood predictors (MLP) and the conditional median predictors (CMP) for each type of censoring. Numerical examples are used to illustrate the prediction methods. Using simulation studies, mean squared prediction error (MSPE) and prediction intervals are generated for these examples. MLP and the CMP are then compared with respect to MSPE and the prediction interval.     

An extension of play against the random past strategy. Choosing the right experts on IBM forecasts

In sequential plays with two players, the players have the opportunity to use information on opponents’ past moves in selecting a move for the current stage. Strategies for Player II are considered in our study, in particular, play against the random past (PRP) strategy. In this paper, PRP strategy will be reviewed and discussed. Hannan consistency of PRP strategy in term of regret (difference in average loss and an envelope loss) on k-extended Bayes envelope risk problem in matching binary bits game will be shown. The simulation of two-experts selection problem on real experts’ forecasting data of IBM share earnings confirms the consistency of PRP strategy.     

Bayesian comparative study on binary time series

In this paper, we consider the Bayesian analysis of binary time series with different priors, namely normal, Students' t, and Jeffreys prior, and compare the results with the frequentist methods through some simulation experiments and one real data on daily rainfall in inches at Mount Washington, NH. Among Bayesian methods, our results show that the Jeffreys prior perform better in most of the situations for both the simulation and the rainfall data. Furthermore, among weakly informative priors considered, Student's t prior with 7 degrees of freedom fits the data most adequately.     

Modeling multivariate cybersecurity risks

Modeling cybersecurity risks is an important, yet challenging, problem. In this paper, we initiate the study of modeling multivariate cybersecurity risks. We develop the first statistical approach, which is centered at a Copula-GARCH model that uses vine copulas to model the multivariate dependence exhibited by real-world cyber attack data. We find that ignoring the due multivariate dependence causes a severe underestimation of cybersecurity risks. Both simulation and empirical studies show that the proposed approach leads to accurate predictions of multivariate cybersecurity risks.     

Robust Bayesian prediction under a general linear-exponential posterior risk function and its application in finite population

We consider robust Bayesian prediction of a function of unobserved data based on observed data under an asymmetric loss function. Under a general linear-exponential posterior risk function, the posterior regret gamma-minimax (PRGM), conditional gamma-minimax (CGM), and most stable (MS) predictors are obtained when the prior distribution belongs to a general class of prior distributions. We use this general form to find the PRGM, CGM, and MS predictors of a general linear combination of the finite population values under LINEX loss function on the basis of two classes of priors in a normal model. Also, under the general ε-contamination class of prior distributions, the PRGM predictor of a general linear combination of the finite population values is obtained. Finally, we provide a real-life example to predict a finite population mean and compare the estimated risk and risk bias of the obtained predictors under the LINEX loss function by a simulation study.     

区域物流需求预测及灰色预测模型的应用

区域物流需求预测是区域物流规划和决策的前提,区域物流需求属于派生需求,由区域经济发展水平决定,因此区域物流需求预测的范围除物流需求本身内容以外,与物流需求紧密相关的各个经济指标也属于区域物流需求预测的范畴。区域物流需求预测有多种预测方法,其中灰色预测模型具有时数据要求限制少、中短期预测精准等特点,特别适合区域物流需求的预测。本文从区域物流需求预测内容、指标选择和预测方法三方面进行了系统研究,首次全面综合地提出了区域物流需求预测的内容及其对应的评价指标,在预测模型的实际应用上具有一定的创新。     

人力资源规划综述

人力资源规划作为企业管理研究中的一个重要课题早已引起国内外众多学者的关注和研究。本文主要阐述了目前此方面的主要研究成果及其在预测方法方面的不足。