Modeling churn using customer lifetime value

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a product-centric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer’s activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable.     

Linguistic modelling and information coarsening based on prototype theory and label semantics

The theory of prototypes provides a new semantic interpretation of vague concepts. In particular, the calculus derived from this interpretation results in the same calculus as label semantics proposed by Lawry. In the theory of prototypes, each basic linguistic label L has the form ‘about P’, where P is a set of prototypes of L and the neighborhood size of the underlying concept is described by the word ‘about’ which represents a probability density function δ on [0,+∞). In this paper we propose an approach to vague information coarsening based on the theory of prototypes. Moreover, we propose a framework for linguistic modelling within the theory of prototypes, in which the rules are concise and transparent. We then present a linguistic rule induction method from training data based on information coarsening and data clustering. Finally, we apply this linguistic modelling method to some benchmark time series prediction problems, which show that our linguistic modelling and information coarsening methods are potentially powerful tools for linguistic modelling and uncertain reasoning.     

Bayesian planning of step‐stress accelerated degradation tests under various optimality criteria

Step‐stress accelerated degradation testing (SSADT) has become a common approach to predicting lifetime for highly reliable products that are unlikely to fail in a reasonable time under use conditions or even elevated stress conditions. In literature, the planning of SSADT has been widely investigated for stochastic degradation processes, such as Wiener processes and gamma processes. In this paper, we model the optimal SSADT planning problem from a Bayesian perspective and optimize test plans by determining both stress levels and the allocation of inspections. Large‐sample approximation is used to derive the asymptotic Bayesian utility functions under 3 planning criteria. A revisited LED lamp example is presented to illustrate our method. The comparison with optimal plans from previous studies demonstrates the necessity of considering the stress levels and inspection allocations simultaneously.     

基于品牌手机未来销量预测

本文是用灰色预测GM（1,1）模型,探讨了手机销售总数量动态变化,为手机生产提供参考。同时用灰色关联度,来计算总销售量对需要预测的品牌影响,以关联度作为权重,来预测目标品牌。结果表明,对手机销售数量的历史趋势拟合程度较高,所以用此预测模型预测,具有一定的参考价值。     

中国福利彩票销售额影响因素分析与基于残差主成分分析的预测

本文应用多元回归分析的方法讨论了中国各省份福利彩票销售额与国民经济、就业人员和职工工资、财政、固定资产投资、人民生活这五个大类中的22个预测变量之间的相关关系,找出了对彩票销售额有显著性影响的变量,此外我们发现,不同年份的数据所得到的残差存在强相关性,本文利用主成分分析的观点提取出了不同年份残差中的公共信息,最后得出彩票销售额的预测方程,利用上述方程对2007年各省份的彩票销售额进行预测,并与真实的销售额数据进行对比,获得了较小的平均相对预报误差.这说明本文所建立的模型确实可以对中国的福利彩票销售额进行有效的预测.     

Use of a tree‐structured hierarchical model for estimation of location and uncertainty in multivariate spatial data

Analysis and modeling of spatial data are of considerable interest in many applications. However, the prediction of geographical features from a set of chemical measurements on a set of geographically distinct samples has never been explored. We report a new, tree‐structured hierarchical model for the estimation of geographical location of spatially distributed samples from their chemical measurements. The tree‐structured hierarchical modeling used in this study involves a set of geographic regions stored in a hierarchical tree structure, with each nonterminal node representing a classifier and each terminal node representing a regression model. Once the tree‐structured model is constructed, given a sample with only chemical measurements available, the predicted regional location of the sample is gradually restricted as it is passed through a series of classification steps. The geographic location of the sample can be predicted using a regression model within the terminal subregion. We show that the tree‐structured modeling approach provides reasonable estimates of geographical region and geographic location for surface water samples taken across the entire USA. Further, the location uncertainty, an estimate of a probability that a test sample could be located within a pre‐estimated, joint prediction interval that is much smaller than the terminal subregion, can also be assessed. Copyright © 2014 John Wiley & Sons, Ltd.     

Computational investigations of folded self-avoiding walks related to protein folding

Various subsets of self-avoiding walks naturally appear when investigating existing methods designed to predict the 3D conformation of a protein of interest. Two such subsets, namely the folded and the unfoldable self-avoiding walks, are studied computationally in this article. We show that these two sets are equal and correspond to the whole n-step self-avoiding walks for n ≤ 14, but that they are different for numerous n ≥ 108, which are common protein lengths. Concrete counterexamples are provided and the computational methods used to discover them are completely detailed. A tool for studying these subsets of walks related to both pivot moves and protein conformations is finally presented.     

Separable convolution template (SCT) background prediction accelerated by CUDA for infrared small target detection

This paper presents a novel background prediction method for infrared small target detection (ISTD). Using a separable convolution template (SCT) to accelerate the traditional background prediction by graphic processing unit (GPU), the new method provides a significant improvement in the prediction speed, which enables the prediction process in real time. And experimental results show its high efficiency and practical application over previous work. The mathematical approach proposed here could be extended to accelerate the applications referred to image convolutions not only to the infrared field.     

An energy-based microstructure model to account for fatigue scatter in polycrystals

Scatter observed in the fatigue response of a nickel-based superalloy, U720, is linked to the variability in the microstructure. Our approach is to model the energy of a persistent slip band (PSB) structure and use its stability with respect to dislocation motion as our failure criterion for fatigue crack initiation. The components that contribute to the energy of the PSB are identified, namely, the stress field resulting from the applied external forces, dislocation pile-ups, and work-hardening of the material is calculated at the continuum scale. Further, energies for dislocations creating slip in the matrix/precipitates, interacting with the GBs, and nucleating/agglomerating within the PSB are computed via molecular dynamics simulations. Through this methodology, fatigue life is predicted based on the energy of the PSB, which inherently accounts for the microstructure of the material. The present approach circumvents the introduction of uncertainty principles in material properties. It builds a framework based on mechanics of microstructure, and from this framework, we construct simulated microstructures based on the measured distributions of grain size, orientation, neighbor information, and grain boundary character, which allows us to calculate fatigue scatter using a deterministic approach. The uniqueness of the approach is that it avoids the large number of parameters prevalent in previous fatigue models. The predicted lives are in excellent agreement with the experimental data validating the model capabilities.     

A fast method for large‐scale De Novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large‐scale experiments is still missing. We introduce a new approach—PEP‐FOLD—to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model‐derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse‐grained energy score. On a benchmark of 52 peptides with 9–23 amino acids, PEP‐FOLD generates lowest‐energy conformations within 2.8 and 2.3 Å Cα root‐mean‐square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27–49 amino acids, PEP‐FOLD reaches an accuracy of 3.6 and 4.6 Å Cα root‐mean‐square deviation for the most‐native and lowest‐energy conformations, using the nonflexible regions identified by NMR. PEP‐FOLD simulations are fast—a few minutes only—opening therefore, the door to in silico large‐scale rational design of new bioactive peptides and miniproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010