Optimal Control and Sensitivity Analysis for Two Risk Models

Two periodic-review models arising in insurance are considered in the framework of cost approach. The first one treats the insurance company performance under assumption of capital injections and reinsurance. The second one deals with assets selling and bank loans. The aim of the article is to demonstrate the method for establishing the optimal control of such applied stochastic models and to study their stability. For this purpose, sensitivity analysis is carried out. Numerical results are also provided.     

Optimal generalized logistic estimator

In this paper, we propose a new efficient estimator namely Optimal Generalized Logistic Estimator (OGLE) for estimating the parameter in a logistic regression model when there exists multicollinearity among explanatory variables. Asymptotic properties of the proposed estimator are also derived. The performance of the proposed estimator over the other existing estimators in respect of Scalar Mean Square Error criterion is examined by conducting a Monte Carlo simulation.     

Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.     

Nearly optimal truncated group sequential test on binomial proportions

In this article, we systematically study the optimal truncated group sequential test on binomial proportions. Through analysis of the cost structure, average test cost is introduced as a new optimality criterion. According to the new criterion, the optimal tests on different design parameters including the boundaries, success discriminant value, stage sample vector, stage size, and the maximum sample size are defined. Since the computation time in finding optimal designs by exhaustive search is intolerably long, group sequential sample space sorting method and procedures are developed to find the near-optimal ones. In comparison with the international standard ISO2859-1, the truncated group sequential designs proposed in this article can reduce the average test costs around 20%.     

Simultaneous inference for treatment regimes

A viable dynamic treatment regime refers to decisions regarding how different treatments and dose levels are tailored through time to match with the patient’s health status. In the therapy for cancer or diseases that require multiple stages of treatments, the effect of preceding treatment (such as growing back of solid tumors or regime-related toxicity) critically influences the selection of treatment in the following stage. So far, analyses of dynamic regimes mainly focus on marginal mean models under the assumption of sequential randomization in a clinical trial. Inference conclusions regarding multiple regimes are normally conservative due to different combinations in the formation of treatment regimes. In this article, we propose a simultaneous confidence interval method to identify treatment regimes that are significantly different from the bulk of the treatment combinations. The new method is applied to analyze the dynamic treatment regimes (DTRs) prostate cancer trial which includes treatment combinations of four chemotherapies at multiple stages. The new method detects a discernible effect of the regime taxane–estramustine–carboplatin (TEC) followed by cyclophosphamide, vincristine, and dexamethasone (CVD), when it is compared with other four regimes starting with CVD.     

Nonparametric estimation of the entropy using a ranked set sample

This article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback–Leibler divergence are provided. Several Monté-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.     

Sensitivity Analyses of the Economic and Economic Statistical Designs of the Multivariate Synthetic T2 Chart

This article performs a sensitivity analyses of the synthetic T² chart using fractional factorial design, which integrates the interaction effects. We are interested in the effects of the input parameters on the optimal cost, chart's parameters, and average run lengths. We also look at the input parameters responsible for the increase in cost and improvement in statistical performance under statistical constraints, and investigate how the input parameters influence the binding effect of the statistical constraints. The sensitivity analyses of the synthetic T² chart are compared with that of the Hotelling's T² chart, and parameters responsible for the cost advantage of the synthetic T² chart are identified.     

Optimal Selection of the Most Reliable Design Based on Gamma Degradation Processes

Manufacturers are often faced with the problem of how to select the most reliable design among several competing designs in the stage of development. It becomes complicated if products are highly reliable. Under the circumstances, recent work has focused on the study with degradation data by assuming that degradation paths follow Wiener processes or random-effect models. However, it is more appropriate to use gamma processes to model degradation data with monotone-increasing pattern. This article deals with the selection problem for such processes. With a minimum probability of correct decision, optimal test plans can be obtained by minimizing the total cost.     

On the optimality of the optimal policies for the deterministic EPQ with partial backordering

In a note published in Omega [Zhang RQ. A note on the deterministic EPQ with partial backordering. Omega 2009;37(5):1036–8], the amended decision procedure for the Pentico et al.'s EPQ with partial backordering (EPQ-PBO) is proposed, by developing another critical value of the backordering rate. However, there is a case when a decision made with this amended procedure is not optimal, which will be shown in this paper. A new decision procedure will be proposed based on the derived necessary and sufficient conditions for considering the policy of losing all sales or the policy to meet all demand as possible optimal decisions. The proposed decision procedure is adapted for one of the extensions of the EPQ-PBO.     

An experimental design criterion for minimizing meta-model prediction errors applied to die casting process design

Summary. We propose the expected integrated mean-squared error (EIMSE) experimental design criterion and show how we used it to design experiments to meet the needs of researchers in die casting engineering. This criterion expresses in a direct way the researchers' goal to minimize the expected meta-model prediction errors, taking into account the effects of both random experimental errors and errors deriving from our uncertainty about the true model form. Because we needed to make assumptions about the prior distribution of model coefficients to estimate the EIMSE, we performed a sensitivity analysis to verify that the relative prediction performance of the design generated was largely insensitive to our assumptions. Also, we discuss briefly the general advantages of EIMSE optimal designs, including lower expected bias errors compared with popular response surface designs and substantially lower variance errors than certain Box–Draper all-bias designs.