On conditional risk estimation considering model risk

Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models.     

Empirical Uncertain Bayes Methods in Area‐level Models

Random effects model can account for the lack of fitting a regression model and increase precision of estimating area‐level means. However, in case that the synthetic mean provides accurate estimates, the prior distribution may inflate an estimation error. Thus, it is desirable to consider the uncertain prior distribution, which is expressed as the mixture of a one‐point distribution and a proper prior distribution. In this paper, we develop an empirical Bayes approach for estimating area‐level means, using the uncertain prior distribution in the context of a natural exponential family, which we call the empirical uncertain Bayes (EUB) method. The regression model considered in this paper includes the Poisson‐gamma and the binomial‐beta, and the normal‐normal (Fay–Herriot) model, which are typically used in small area estimation. We obtain the estimators of hyperparameters based on the marginal likelihood by using a well‐known expectation‐maximization algorithm and propose the EUB estimators of area means. For risk evaluation of the EUB estimator, we derive a second‐order unbiased estimator of a conditional mean squared error by using some techniques of numerical calculation. Through simulation studies and real data applications, we evaluate a performance of the EUB estimator and compare it with the usual empirical Bayes estimator.     

Statistical inference concerning weighted Poisson rates under some natural order restrictions

A conditional Poisson model is proposed for situations where one wants to compare the rate parameters for several populations, adjusting for a 'size' parameter and the random e ects of the subjects. Sometimes the physical nature of the problem makes it logical to consider some order restrictions on the rate parameters. The rate parameters are estimated and tested for under such order restrictions. This investigation is motivated by a real-life example on butterfly behavior, and the estimation and tests of hypotheses are illustrated over this data set.     

Non-parametric Regression with Dependent Censored Data

Abstract.  Let ( X _i , Y _i ) ( i = 1 ,…, n ) be n replications of a random vector ( X , Y  ), where Y is supposed to be subject to random right censoring. The data ( X _i , Y _i ) are assumed to come from a stationary α -mixing process. We consider the problem of estimating the function m ( x ) = E ( φ ( Y ) |  X = x ), for some known transformation φ . This problem is approached in the following way: first, we introduce a transformed variable     , that is not subject to censoring and satisfies the relation     , and then we estimate m ( x ) by applying local linear regression techniques. As a by-product, we obtain a general result on the uniform rate of convergence of kernel type estimators of functionals of an unknown distribution function, under strong mixing assumptions.     

Asymptotic Expansion and Conditional Robustness for the Sample Multiple Correlation Coefficient Under Nonnormality

ABSTRACT

Asymptotic distributions of the standardized estimators of the squared and non squared multiple correlation coefficients under nonnormality were obtained using Edgeworth expansion up to O(1/n). Conditions for the normal-theory asymptotic biases and variances to hold under nonnormality were derived with respect to the parameter values and the weighted sum of the cumulants of associated variables. The condition for the cumulants indicates a compensatory effect to yield the robust normal-theory lower-order cumulants. Simulations were performed to see the usefulness of the formulas of the asymptotic expansions using the model with the asymptotic robustness under nonnormality, which showed that the approximations by Edgeworth expansions were satisfactory.     

Quality of Fit Measures in the Framework of Quantile Regression

Abstract. In regression experiments, to learn about the strength of the relationship between a covariate vector and a dependent variable, we propose a ‘coefficient of determination’ based on the quantiles. Such a coefficient is a ‘local’ measure in the sense that the strength is measured at a prespecified quantile level. Once estimated, it can be used, for example, to measure the relative importance of a subset of covariates in the quantile regression context. Related to this coefficient, we also propose a new ‘local’ lack‐of‐fit measure of a given parametric model. We provide some asymptotic results of the proposed measures and carry out a Monte Carlo simulation study to illustrate their use and performance in practice.     

EXTENDING THE BOUNDARIES OF OPERATIONS MANAGEMENT: AN INTERNATIONAL FIELD-STUDIES APPROACH INTEGRATING INFORMATION SYSTEMS

Continued globalization, extensive use of information technologies, increasing cross-functional dependencies, and new organizational forms are rapidly changing the learning context of operations management. This paper describes an example of a mba elective course that has been created to address and learn from these new realities. We have taken a process view of the global firm, focusing mainly on the new product/service development, order fulfillment, and supply chain/ after-sales service processes. We have designed our course to contain most of the traditional topics in operations management (om) with information systems (is) woven in to support the key business processes. Our underlying belief is that these two functional areas should be seen as a blend, a seamless merging of the two fields, to obtain a competitive advantage. Besides teaching om and is in this integrated manner, the course engages in face-to-face student consulting projects with multinational companies. This paper describes our rationale for the course design and how it has been implemented over the last 6 years-as well as our plans for the future.     

Cognitive and organizational ergonomics in the transition of the new integrated center of control of an oil refinery: human reliability and administration of changes

The conception of a product is closely tied to its adaptation level to the users. In this view, designers are increasingly oriented to survey the needs and features of the users. This paper aims at developing a diagnosis of employees working in high-complexity activities in a petrochemical company, in light of the physical and operating changes in the Integrated Center of Control; assessing the reception sensibility to changes; assessing the cognitive pattern of the group; and making suggestions that might eliminate or minimize the difficulties in the transition process of the change, in order to reduce the adaptation period. The field of study comprised 111 production, transfer and storage operators, forming 5 groups of desktop activities. The stages of the study followed the following flow: survey of the prescribed tasks and organizational structure; Concentrated Attention test; application of the Work and Disease Risks Inventory (ITRA, Portuguese acronym); and structured psychological interview. The ITRA results pointed to a serious cognitive cost (3.83) for all five groups, this being the largest intervention focus. The items: division of task contents (3.52), social professional relationships (2.93), quality of the physical environment (2.91), physical cost (3.24), emotional cost (2.71), freedom of expression (3.77), professional fulfillment (3.41); experience and suffering (2.75), lack of recognition (2.18) and physical injuries (2.07) were considered critical. Meanwhile, social damages (1.64) and psychological injuries (1.35) are bearable. As to the Concentrated Attention test, most workers registered average level. In the individual interviews, workers showed that larger involvement in the process of physical, organizational and operational change in the desktops and on field works was required, as well as the follow up of implementations, so as to reduce the adaptation process and prevent rework (furniture, equipment, noise, form of communication with the area and training for the changes). As a result of the findings handed to the management and returned to the workers, several actions were implemented on ergonomic non-conformities found in the analysis. It is possible to prove the importance of the insertion of cognitive and organizational ergonomics in the building projects of new facilities for high-complexity activities in petrochemical plants.     

Matching pseudocounts for interval estimation of binomial and Poisson parameters

ABSTRACT

For interval estimation of a binomial proportion and a Poisson mean, matching pseudocounts are derived, which give the one-sided Wald confidence intervals with second-order accuracy. The confidence intervals remove the bias of coverage probabilities given by the score confidence intervals. Partial poor behavior of the confidence intervals by the matching pseudocounts is corrected by hybrid methods using the score confidence interval depending on sample values.     

Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing

Abstract. In the context of multivariate mean regression, we propose a new method to measure and estimate the inadequacy of a given parametric model. The measure is basically the missed fraction of variation after adjusting the best possible parametric model from a given family. The proposed approach is based on the minimum L ² ‐distance between the true but unknown regression curve and a given model. The estimation method is based on local polynomial averaging of residuals with a polynomial degree that increases with the dimension d of the covariate. For any d ≥ 1 and under some weak assumptions we give a Bahadur‐type representation of the estimator from which ‐consistency and asymptotic normality are derived for strongly mixing variables. We report the outcomes of a simulation study that aims at checking the finite sample properties of these techniques. We present the analysis of a dataset on ultrasonic calibration for illustration.