Applied Stochastic Models in Business and Industry

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2195

No abstract is available for this article.

Gao, Xiaoli; Ahmed, S. E.; Feng, Yang

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2193

In high‐dimensional data settings where p ≫ n, many penalized regularization approaches were studied for simultaneous variable selection and estimation. However, with the existence of covariates with weak effect, many existing variable selection methods, including Lasso and its generations, cannot distinguish covariates with weak and no contribution. Thus, prediction based on a subset model of selected covariates only can be inefficient. In this paper, we propose a post selection shrinkage estimation strategy to improve the prediction performance of a selected subset model. Such a post selection shrinkage estimator (PSE) is data adaptive and constructed by shrinking a post selection weighted ridge estimator in the direction of a selected candidate subset. Under an asymptotic distributional quadratic risk criterion, its prediction performance is explored analytically. We show that the proposed post selection PSE performs better than the post selection weighted ridge estimator. More importantly, it improves the prediction performance of any candidate subset model selected from most existing Lasso‐type variable selection methods significantly. The relative performance of the post selection PSE is demonstrated by both simulation studies and real‐data analysis. Copyright © 2016 John Wiley & Sons, Ltd.

Fan, Jianqing

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2224

Qiu, Peihua; Yang, Kai; You, Lu

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2220

Li, Yanming; Hong, Hyokyoung Grace; Li, Yi

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2216

Kjell, Doksum; Fujimura, Joan

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2214

Gao, Xiaoli; Ejaz Ahmed, S.; Feng, Yang

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2245

Leisen, Fabrizio; Marin, J. Miguel; Villa, Cristiano

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2227

Insurance risks data typically exhibit skewed behaviour. In this paper, we propose a Bayesian approach to capture the main features of these data sets. This work extends a methodology recently introduced in the literature by considering an extra parameter that captures the skewness of the data. In particular, a skewed Student‐t distribution is considered. Two data sets are analysed: the Danish fire losses and the US indemnity loss. The analysis is carried with an objective Bayesian approach. For the discrete parameter representing the number of the degrees of freedom, we adopt a novel prior recently appeared in the literature. Copyright © 2017 John Wiley & Sons, Ltd.

Temnov, Grigory

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2229

We propose a strategy for automated trading, outline theoretical justification of the profitability of this strategy, and overview the backtesting results in application to foreign currencies trading. The proposed methodology relies on the assumption that processes reflecting the dynamics of currency exchange rates are in a certain sense similar to the class of Ornstein–Uhlenbeck processes and exhibit the mean reverting property. In order to describe the quantitative characteristics of the projected return of the strategy, we derive the explicit expression for the running maximum of the Ornstein–Uhlenbeck process stopped at maximum drawdown and look at the correspondence between derived characteristics and the observed ones. Copyright © 2017 John Wiley & Sons, Ltd.

Wan, Guangyu; Wang, Qinan

2017 Applied Stochastic Models in Business and Industry

doi: 10.1002/asmb.2231

Waiting has been a significant concern for healthcare services. We address this issue in the context of a two‐tier service system in this study. A two‐tier healthcare service system consists of two different service providers, typically one public service provider and one private service provider. In a baseline model, the two service providers are modeled by two queue servers, which charge each patient a common fixed fee for the service. Then, we study a queue model in which one service provider offers a subsidy or charges a premium while the other maintains the fixed service fee. This system provides a mechanism to segment patients along their waiting time cost through price discrimination. We analyze the problem from both the perspective of minimizing total waiting cost for all patients and the perspective of maximizing social gain for the public service provider or profit for the private service provider. We show that this model can significantly alleviate the burden of waiting for patients. The study addresses the design, the efficiency, and the implementation of two‐tier healthcare service systems. Copyright © 2017 John Wiley & Sons, Ltd.