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.     

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.     

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.     

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.     

Selection of the Ridge Parameter Using Mathematical Programming

Ridge regression solves multicollinearity problems by introducing a biasing parameter that is called ridge parameter; it shrinks the estimates and their standard errors in order to reach acceptable results. Selection of the ridge parameter was done using several subjective and objective techniques that are concerned with certain criteria. In this study, selection of the ridge parameter depends on other important statistical measures to reach a better value of the ridge parameter. The proposed ridge parameter selection technique depends on a mathematical programming model and the results are evaluated using a simulation study. The performance of the proposed method is good when the error variance is greater than or equal to one; the sample consists of 20 observations, the number of explanatory variables in the model is 2, and there is a very strong correlation between the two explanatory variables.     

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.     

Food fights: irritating for social change among Auckland's alternative food initiatives

ABSTRACT

This article explores alternative food initiatives (AFI) and their performances of benign transgression. Through collaborative activist-and-academic-storytelling we tease apart the divergent practices of AFIs to question what mediates these performances in the grey area between conventional and alternative practice. Grounded examples of AFIs performing alternative economy and related acts of ‘irritant’ civil disobedience show how subverting normative practices of power and authority can catalyse social reproduction of difference, and tangibly alter the conventional food system.