Browsing by Subject "Robustness"
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Item Forecast combination for outlier protection and forecast combination under heavy tailed errors(2014-11) Cheng, GangForecast combination has been proven to be a very important technique to obtain accurate predictions. Numerous forecast combination schemes with distinct properties have been proposed. However, to our knowledge, little has been discussed in the literature on combining forecasts with minimizing the occurrence of forecast outliers in mind. An unnoticed phenomenon is that robust combining, which often improves predictive accuracy (under square or absolute error loss) when innovation errors have a tail heavier than a normal distribution, may have a higher frequency of prediction outliers. Given the importance of reducing outlier forecasts, it is desirable to seek new loss functions to achieve both the usual accuracy and outlier-protection simultaneously.In the second part of this dissertation, we propose a synthetic loss function and apply it on a general adaptive theoretical and numeric results support the advantages of the new method in terms of providing combined forecasts with relatively fewer large forecast errors and comparable overall performances. For various reasons, in many applications, forecast errors exhibit heavy tail behaviors. Unfortunately, to our knowledge, little has been done to deal with forecast combination for such situations. The familiar forecast combination methods such as simple average, least squares regression, or those based on variance-covariance of the forecasts, may perform very poorly in such situations. In the third part of this dissertation, we propose two forecast combination methods to address the problem. One is specially proposed for the situations that the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student's t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to shortage of data and/or evolving data generating process. adaptive risk bounds of both methods are developed. Simulations and a real example show the excellent performance of the new methods.Item Mechanisms for access control and application-level recovery in context-aware applications(2009-12) Kulkarni, Devdatta J.Context-awareness is a central characteristic of several emerging application domains, characterizing the applications’ ability to adapt and perform tasks based on ambient context conditions. Context refers to a situation in the physical or the virtual world that may be utilized by an application for the purpose of dynamic adaptation, for example, to acquire services needed in a given location. While the envisioned advantages of context-awareness are significant, providing access control and robustness guarantees for context-aware applications is a difficult task. This is because of the inherent dynamic nature of such applications and the environments in which they are deployed. In this thesis we develop models and mechanisms for addressing the access control and robustness problems in context-aware applications. We also develop a programming framework for building context-aware applications from their high-level design specifications. An access control model for context-aware applications needs to support specification and enforcement of context-based access control policies. Such policies are related to assignment of context-based access privileges to users, access control for services that are dynamically integrated with an application, and context-based constraining of access to resources managed by a service. The first contribution of this thesis is the development of a context-aware role-based access control model (CA-RBAC) that addresses the above requirements of such applications. We identify the context invalidation problem associated with correct enforcement of contextbased access control requirements, and develop a mechanism to address it. Robustness of context-aware applications is affected due to failures in discovering the required resources and services during a context-driven reconfiguration, service crashes, and exceptions thrown by a service. Moreover, concurrent handling of context events can affect an application’s correct behavior, if not properly coordinated. The second contribution of this thesis is the development of an application-level programmed error recovery model for such applications. This model combines asynchronous event handling with synchronous exception handling for building robust context-aware applications. A novel mechanism in the form of an exception interface is provided for roles through which users may participate in executing recovery tasks. The third contribution of this thesis is the design and implementation of a generative programming framework for building context-aware applications from their high-level design specifications. The CA-RBAC model and the programmed error recovery mechanisms are integrated in this programming framework. This framework enables rapid construction of context-aware applications using a policy-driven middleware.Item Robust Predictions in Dynamic Games(2018-08) Ruiz Gómez, DavidThis dissertation is about understanding the robustness property of predictions to misspecification of higher-order beliefs in dynamic games with payoff uncertainty. In particular, it asks: Which simplifying assumptions about beliefs provide robust predictions in dynamic games? The most important result of this dissertation, presented in the second chapter, is to show that lack of robustness is a generic property of predictions consistent with (interim) sequential rationalizability (ISR) unless the prediction is unique. I consider this to be an essential and novel contribution to the literature of robustness in game theory since it challenges the validity of the standard approach to modeling uncertainty in dynamic games because it gives rise, for almost every model of uncertainty, to spurious predictions. Typically, when analyzing a model, different parameters represent different assumptions of the model, and therefore, predictions from the model are sensitive to the specification of those parameters. For example, it is well known that in the standard Bayesian approach to games with incomplete information, a crucial parameter that requires to be specified, and at the same time is neither observable nor verifiable without any error from the point of view of researchers, is players beliefs and hierarchies of beliefs; hence, because of the previous observation, it happens that in many applications hierarchies of beliefs encode strong (informational) assumptions, and as I already mentioned, behavioral predictions (e.g., in the form of Perfect Bayesian Equilibrium, Interim Sequential Rationalizability, among others) depend on those assumptions; moreover, in some cases, this dependence can be very sensitive at the tails of the hierarchies of beliefs specified in the model. The robustness property refers, in this case, to the possibility of guarantee that slight changes in the specification of the parameters do not lead to significant changes in predictions, since at least from a methodological point of view, if the property holds generically it would provide a justification for the validity of the standard approach to model uncertainty. One approach to understanding the robustness property of set-valued solution concepts in games is to ask: Which predictions remain valid after all common certain-belief assumptions are relaxed? Penta (2012) have shown that in (finite) dynamic games with incomplete information the only predictions that remain valid after relaxing all the assumptions about beliefs and hierarchies of beliefs are those consistent with (interim) sequential rationalizability. In other words, ISR is the strongest solution concept such that, for every model of beliefs it is possible to guarantee that an outcome that was ruled out by ISR is ruled out for every approximation of the model. This result implies lack of robustness of any refinement of ISR as, for example, any of the familiar equilibrium concepts. In this dissertation, a stronger notion of robustness is considered, that is if, in addition, it is possible to guarantee that there are no spurious predictions, in the sense that for every predicted outcome of a solution concept there is no approximation ruling that outcome out. This last notion is formalized through a notion of full continuity of predictions with respect to beliefs and hierarchies of beliefs. An approach to this question for static games was given in Ely and Peski (2011). They introduced the concept of critical types as precisely those assumptions on beliefs that are vulnerable to misspecification, that is, as those types to which there are spurious predictions consistent with rationalizability. They showed that critical types are non-generic (rare). The key argument in Ely and Peski's result exploits the fact that, in static games, rationalizability does not depend on the timing of the arrival of players' information. However, in dynamic games, ISR does depend on the timing of information. In particular, players beliefs are restricted only at the beginning of the game, and via conditioning whenever possible. However, at zero probability events, conditional beliefs are unrestricted. I exploit this observation to show that Ely and Peski's result does not hold in dynamic settings: lack of robustness is a generic property of ISR whenever it delivers multiple predictions. As ISR often delivers multiple predictions in applications, this result casts doubts on the interpretation and validity of solution concepts such as Perfect Bayesian Equilibrium, Sequential Equilibrium, and ISR itself. By acknowledging model misspecification of higher-order beliefs, there is no type in Harsanyi's framework at which a researcher can guarantee that no slight perturbation on the modeling assumptions exists which rules some prediction out unless the prediction is unique. Finally, I propose an ongoing research agenda in the problem of robust predictions in dynamic games. In particular, we consider dynamic games with payoff uncertainty and, as in Siniscalchi (2016a,b), assume that players in the game choose strategies according to structural rationality. Players with structural preferences induce, at the beginning of the game, a collection of alternative hypothesis about how the game is going to unfold; and rank any two strategies depending on the expected payoff under those alternative priors in a lexicographic way. A strategy is structurally rational if it is maximal. We propose to study general properties of (weak) interim structural rationalizability (IStR), a solution concept that characterizes the behavioral implications of common certainty in structural rationality. In the case of Bayesian dynamic games with incomplete information, we are particularly interested in the robustness properties of IStR to perturbations of higher-order beliefs. As illustrated by an example, three results are conjecture: a structure theorem of structural rationalizability, a characterization of critical types, and a non-generic result of the set of critical types.Item Time delay margin analysis for adaptive flight control laws.(2010-12) Dorobantu, AndreiAdaptive control algorithms have the potential to improve performance and reliability in flight control systems. Implementation of adaptive control on commercial and military aircraft requires validation and verification of the control system's robustness to modeling error and uncertainty. Currently, there is a lack of tools available to rigorously analyze the robustness of adaptive systems due to their inherently nonlinear dynamics. This thesis addresses the use of nonlinear robustness analysis for adaptive flight control systems. First, a model reference adaptive controller is derived for an aircraft short period model. It is noted that the controller is governed by polynomial dynamics. Polynomial optimization tools are then applied to the closed-loop model to assess its robustness to time delays. Time delay margins are computed for various tuning of design parameters in the adaptive law, as well as in the presence of model uncertainty.