The design of attribute sampling inspection plans based on compressed or narrow limits for food safety applications is covered. Artificially compressed limits allow a significant reduction in the number of analytical tests to be carried out while maintaining the risks at predefined levels. The design of optimal sampling plans is discussed for two given points on the operating characteristic curve and especially for the zero acceptance number case. Compressed limit plans matching the attribute plans of the International Commission on Microbiological Specifications for Foods are also given. The case of unknown batch standard deviation is also discussed. Three-class attribute plans with optimal positions for given microbiological limit *M* and good manufacturing practices limit *m* are derived. The proposed plans are illustrated through examples. R software codes to obtain sampling plans are also given. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling has often failed to meet expectations, mostly because of the difficulty of comprehending relationships within phenomena and expressing them in mathematical models. Reality is frequently too complex to be reflected in a single model. This is often the case of marketing research, where variables relating to socioeconomics or psychographics constitute potential sources of heterogeneity. In such cases, the assumption of ‘one model fits all’ is unrealistic and may lead to inaccurate decisions. Thus, heterogeneity is a major issue in modeling. Once a model has been fitted to a complete data set that fulfills all validation criteria, it is difficult to establish whether it is valid for the whole population or it is merely an average artifact from several sub-populations. The purpose of this paper is to present the Pathmox approach to deal with heterogeneity in partial least squares path modeling. The idea behind Pathmox is to build a tree of path models that have look-alike structure as a binary decision tree, with different models for each of its nodes. The split criterion consists of an *F* statistic comparing two structural models. In order to ensure the suitability of the split criterion, a simulation study was conducted. Finally, we have applied Pathmox to a survey that measured *Satisfaction* among Spanish mobile phone operators. Results suggest that the Pathmox approach performs adequately in detecting partial least squares path modeling heterogeneity. Copyright © 2016 John Wiley & Sons, Ltd.

Reference samples are frequently used to estimate in-control parameters, which are then used as the true in-control parameters during the monitoring phase of Statistical Process Control (SPC) applications. The SPC literature has recognized that even small errors in parameter estimates determined from reference samples can have a large impact on the conditional (given the values of the estimated parameters) in-control average run length. However, there is little quantitative guidance on how large the reference sample should be to minimize this impact. In this paper, under the context of a recently developed Cumulative Sum (CUSUM) designed to detect translations in exponential distributions, a reference sample size formula for controlling relative error of the conditional in-control average run length is derived. The result in this paper is a stepping stone for reference sample size formulas in more general settings. Copyright © 2016 John Wiley & Sons, Ltd.

]]>Distribution-free (nonparametric) control charts are helpful in applications where we do not have enough information about the underlying distribution. The Shewhart precedence charts is a class of Phase I nonparametric charts for location. One of these charts, called the median precedence chart (Med chart hereafter), uses the median of the test sample as the charting statistic, whereas another chart, called the minimum precedence chart (Min chart hereafter), uses the minimum. In this paper, we first study the comparative performance of the Min and the Med charts, respectively, in terms of their in-control and out-of-control run-length properties in an extensive simulation study. It is seen that neither chart is best as each has its strength in certain situations. Next, we consider enhancing their performance by adding some supplementary runs-rules. It is seen that the new charts present very attractive run-length properties, that is, they outperform their competitors in many situations. A summary and some concluding remarks are given. Copyright © 2016 John Wiley & Sons, Ltd.

]]>We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these *dynamic dependence network* models shows how the individual series can be *decoupled* for sequential analysis and then *recoupled* for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked – for later recoupling – through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currencies, stock indices and commodity prices, the case study includes evaluations of multi-day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks. Copyright © 2016 John Wiley & Sons, Ltd.

Definitive screening designs (DSDs) are a class of experimental designs that allow the estimation of linear, quadratic, and interaction effects with little experimental effort if there is effect sparsity. The number of experimental runs is twice the number of factors of interest plus one. Many industrial experiments involve nonnormal responses. Generalized linear models (GLMs) are a useful alternative for analyzing these kind of data. The analysis of GLMs is based on asymptotic theory, something very debatable, for example, in the case of the DSD with only 13 experimental runs. So far, analysis of DSDs considers a normal response. In this work, we show a five-step strategy that makes use of tools coming from the Bayesian approach to analyze this kind of experiment when the response is nonnormal. We consider the case of binomial, gamma, and Poisson responses without having to resort to asymptotic approximations. We use posterior odds that effects are active and posterior probability intervals for the effects and use them to evaluate the significance of the effects. We also combine the results of the Bayesian procedure with the lasso estimation procedure to enhance the scope of the method. Copyright © 2016 John Wiley & Sons, Ltd.

]]>This paper is concerned with the modeling of advertiser behaviors in sponsored search. Modeling advertiser behaviors can help search engines better serve advertisers, improve auction mechanism, and forecast future revenue. Previous works on this topic either unrealistically assume advertisers to be able to perceive the states of the sponsored search system and the private information of other advertisers or ignore the differences in advertisers' abilities to optimize their bid strategies. To tackle the problems, we propose viewing sponsored search auctions as partially observable multi-agent system with private information. Then, we employ a reinforcement learning behavior model to describe how each advertiser responds to this multi-agent system. The proposed model no longer assumes advertisers to have perfect information access, but instead assumes them to optimize their strategies only based on the partially observed states in the auctions. Furthermore, the model does not specify how the optimization is conducted, but instead uses parameters learned from data to describe different advertisers' abilities in obtaining the optimal strategies. Our experiments on real sponsored search data demonstrate that the proposed model outperforms previous models in predicting the bids and rank positions of the advertisers in the near future. In addition to the accurate prediction of these short-term behaviors, our study shows another nice property of the proposed model. That is, if all the advertisers behave according to the model, the multi-agent system of sponsored search will converge to a locally envy-free equilibrium, under certain conditions. This result establishes a connection between machine-learned behavior models and game-theoretic properties of the system. Copyright © 2016 John Wiley & Sons, Ltd.

]]>Predicting weekly box-office demand is an important yet challenging question. For theater exhibitors, such information will enhance negotiation options with distributers, and assist in planning weekly movie portfolio mix. Existing literature focuses on forecasts of pre-released total gross revenue or on weekly predictions based on first-weeks observations. This work adds to the literature in modeling the entire demand structure forecasts by utilizing information on movie similarity network. Specifically, we draw upon the assumption that aggregated consumers' choice in the film industry is the main key in understanding movies' demand. Therefore, similar movies, in terms of audience appeal, should yield similar demand structure. In this work, we propose an automated technique that derives measurements of demand structure. We demonstrate that our technique enables to analyze different aspects of demand structure, namely, decay rate, time of first demand peak, per-screen gross value at peak time, existence of second demand wave, and time on screens. We deploy ideas from variable selection procedures, to investigate the prediction power of similarity network on demand dynamics. We show that not only our models perform significantly better than models that discard the similarity network but are also robust to new sets of box-office movies. Copyright © 2016 John Wiley & Sons, Ltd.

]]>Increased consumption of fossil fuels in industrial production has led to a significant elevation in the emission of greenhouse gases and to global warming. The most effective international action against global warming is the Kyoto Protocol, which aims to reduce carbon emissions to desired levels in a certain time span. Carbon trading is one of the mechanisms used to achieve the desired reductions. One of the most important implications of carbon trading for industrial systems is the risk of uncertainty about the prices of carbon allowance permits traded in the carbon markets. In this paper, we consider stochastic and time series modeling of carbon market prices and provide estimates of the model parameters involved, based on the European Union emissions trading scheme carbon allowances data obtained for 2008–2012 period. In particular, we consider fractional Brownian motion and autoregressive moving average–generalized autoregressive conditional heteroskedastic modeling of the European Union emissions trading scheme data and provide comparisons with benchmark models. Our analysis reveals evidence for structural changes in the underlying models in the span of the years 2008–2012. Data-driven methods for identifying possible change-points in the underlying models are employed, and a detailed analysis is provided. Our analysis indicated change-points in the European Union Allowance (EUA) prices in the first half of 2009 and in the second half of 2011, whereas in the Certified Emissions Reduction (CER) prices three change-points have appeared, in the first half of 2009, the middle of 2011, and in the second half of 2012. These change-points seem to parallel the global economic indicators as well. Copyright © 2016 John Wiley & Sons, Ltd.

]]>We consider the problem of estimating occurrence rates of rare events for extremely sparse data using pre-existing hierarchies and selected features to perform inference along multiple dimensions. In particular, we focus on the problem of estimating click rates for {Advertiser, Publisher, and User} tuples where both the Advertisers and the Publishers are organized as hierarchies that capture broad contextual information at different levels of granularities. Typically, the click rates are low, and the coverage of the hierarchies and dimensions is sparse. To overcome these difficulties, we decompose the joint prior of the three-dimensional click-through rate using tensor decomposition and propose a multidimensional hierarchical Bayesian framework (abbreviated as MadHab). We set up a specific framework of each dimension to model dimension-specific characteristics. More specifically, we consider the hierarchical beta process prior for the Advertiser dimension and for the Publisher dimension respectively and a feature-dependent mixture model for the User dimension. Besides the centralized implementation, we propose two distributed algorithms through MapReduce and Spark for inferences, which make the model highly scalable and suited for large scale data mining applications. We demonstrate that on a real world ads campaign platform, our framework can effectively discriminate extremely rare events in terms of their click propensity. Copyright © 2016 John Wiley & Sons, Ltd.

]]>The paper deals with the mathematical modeling of the reaction mechanism of rust formation. We provide both a quantitative description based on probability theory and a qualitative description for rust evolution using differential geometry. Copyright © 2016 John Wiley & Sons, Ltd.

]]>No abstract is available for this article.

]]>Degradation data have been widely used to estimate product reliability. Because of technology advancement, time-varying usage and environmental variables, which are called dynamic covariates, can be easily recorded nowadays, in addition to the traditional degradation measurements. The use of dynamic covariates is appealing because they have the potential to explain more variability in degradation paths. We propose a class of general path models to incorporate dynamic covariates for modeling of degradation paths. Physically motivated nonlinear functions are used to describe the degradation paths, and random effects are used to describe unit-to-unit variability. The covariate effects are modeled by shape-restricted splines. The estimation of unknown model parameters is challenging because of the involvement of nonlinear relationships, random effects, and shaped-restricted splines. We develop an efficient procedure for parameter estimations. The performance of the proposed method is evaluated by simulations. An outdoor coating weathering dataset is used to illustrate the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

]]>This paper proposes and makes a study of a new model for volatility index option pricing. Factors such as mean-reversion, jumps, and stochastic volatility are taken into consideration. In particular, the positive volatility skew is addressed by the jump and the stochastic volatility of volatility. Daily calibration is used to check whether the model fits market prices and generates positive volatility skews. Overall, the results show that the mean-reverting logarithmic jump and stochastic volatility model (called MRLRJSV in the paper) serves as the best model in all the required aspects. Copyright © 2015 John Wiley & Sons, Ltd.

]]>This paper proposes a new model that generalizes the linear sliding window system to the case of multiple failures. The considered ** k**-within-

This paper deals with the problem of allocating spare components for improving the system reliability when lifetimes of components are dependent. Two policies, called *active* and *standby* redundancies, are investigated in details. The optimal allocations are given without specific assumptions on the dependency structure among the component lifetimes. When there exist the (positive/negative) quadratic dependence and (upper/lower) orthant order among the component lifetimes, the derived results are simplified. Various illustrative examples are also given. Copyright © 2015 John Wiley & Sons, Ltd.

This paper addresses the non-parametric estimation of the stochastic process related to the classification problem that arises in robot programming by demonstration of compliant motion tasks. Robot programming by demonstration is a robot programming paradigm in which a human operator demonstrates the task to be performed by the robot. In such demonstration, several observable variables, such as velocities and forces can be modeled, non-parametrically, in order to classify the current state of a contact between an object manipulated by the robot and the environment in which it operates. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states made during a demonstration, called contact classification. We propose a contact classification algorithm based on the random forest algorithm. The main advantage of this approach is that it does not depend on the geometric model of the objects involved in the demonstration. Moreover, it does not rely on the kinestatic model of the contact interactions. The comparison with state-of-the-art contact classifiers shows that random forest classifier is more accurate. Copyright © 2015 John Wiley & Sons, Ltd.

]]>This paper introduces some methods for outlier identification in the regression setting, motivated by the analysis of steelmaking process data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening with univariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is based on quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditional quantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity, allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experiments have been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by some real life examples, taking as the response variable the energy consumed in the melting process. Copyright © 2015 John Wiley & Sons, Ltd.

]]>In this paper, we investigate the defined benefit pension plan, where the object of the manager is to minimise the contribution rate risk and the solvency risk by considering a quadratic performance criterion. To incorporate some well-documented behavioural features of human beings, we consider the situation where the discounting is non-exponential. It leads to a time-inconsistent control problem in the sense that the Bellman optimality principle does no longer hold. In our model, we assume that the benefit outgo is constant, and the pension fund can be invested in a risk-free asset and a risky asset whose return follows a geometric Brownian motion. We characterise the time-consistent strategies and value function in terms of the solution of a system of integral equations. The existence and uniqueness of the solution is verified, and the approximation of the solution is obtained. Some numerical results of the equilibrium contribution rate and equilibrium investment policy are presented for three types of discount functions. Copyright © 2015 John Wiley & Sons, Ltd.

]]>We propose a special panel quantile regression model with multiple stochastic change-points to analyze latent structural breaks in the short-term post-offering price–volume relationships in China's growth enterprise market where the piecewise quantile equations are defined by change point indication functions. We also develop a new Bayesian inference and Markov chain Monte Carlo simulation approach to estimate the parameters, including the locations of change points, and put forth simulation-based posterior Bayesian factor tests to find the best number of change points. Our empirical evidence suggests that the single change point effect is significant on quantile-based price–volume relationships in China's growth enterprise market. The lagged initial public offering (IPO) return and the IPO volume rate of change have positive impacts on the current IPO return before and after the change point. Along with investors' gradually declining hot sentiment toward a new IPO, the market index volume rate of change induces the abnormal short-term post-offering IPO return to move back to the equilibrium. Copyright © 2015 John Wiley & Sons, Ltd.

]]>A common practice in customer satisfaction analysis is to administer surveys where subjects are asked to express opinions on a number of statements, or satisfaction scales, by use of ordered categorical responses. Motivated by this application, we propose a pseudo-likelihood approach to estimate the dependence structure among multivariate categorical variables. As it is commonly carried out in this area, we assume that the responses are related to latent continuous variables that are truncated to induce categorical responses. A Gaussian likelihood is assumed for the latent variables leading to the so-called ordered probit model. Because the calculation of the exact likelihood is computationally demanding, we adopt an approximate solution based on pairwise likelihood. To asses the performance of the approach, simulation studies are conducted comparing the proposed method with standard likelihood methods. A parametric bootstrap approach to evaluate the variance of the maximum pairwise likelihood estimator is proposed and discussed. An application to customer satisfaction survey is performed showing the effectiveness of the approach in the presence of covariates and under other generalizations of the model. Copyright © 2015 John Wiley & Sons, Ltd.

]]>A new approach to optimal maintenance of multistate systems (networks) with binary components is developed. Univariate and multivariate signatures are used for description of system's structure and for efficient dealing with corresponding optimal maintenance problems. A system is subject to a shock process. Each shock destroys in a random way one of the operating components. The first strategy is to perform the preventive maintenance after the system enters one of the intermediate states between the initial *UP* state and the final absorbing *DOWN* state. The second strategy is to carry out preventive maintenance after the system experiences *k* shocks. Some illustrations and numerical results are presented. Copyright © 2015 John Wiley & Sons, Ltd.

Many criteria of ageing for random variables or vectors have been proposed in the literature over many years. For instance, a random variable is increasing in failure rate (IFR) if, and only if, it can be ordered with an exponentially distributed random variable in the classical univariate convex transform order. A new multivariate generalization of the convex transform order has recently been proposed in the literature. In this work, we propose a new multivariate IFR notion for multivariate distributions based on comparisons in this new order with a properly defined exponentially distributed random vector. Properties, applications, and illustrations of this new notion are given as well. Copyright © 2016 John Wiley & Sons, Ltd.

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