Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 162 for “"Misspecification"”.
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Addressing misspecification in contextual optimization
We study the predict-then-optimize framework approach, which combines machine learning and a downstream optimization task. This approach entails forecasting unknown parameters of an optimization problem and then resolving the optimization task based on these predictions. For example, consider an …
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Misspecification testing in systems of equations
… be orthogonal under the null hypothesis of no misspecification, and the sample analog of these functions is used as a basis for constructing misspecification tests. As an extension of this idea, it is argued that viewing the model in explicit probabilistic terms provides a basis for developing …
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Sequential design augmentation with model misspecification
… of either correctly specified models or misspecification in the user’s model that can be quantified, such as using a first order model when a second order model is correct. However, under model misspecification the sequential placement of points in the new region of interest using usual …
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Model Misspecification and the Hedging of Exotic Options
Asset pricing models are well established and have been used extensively by practitioners both for pricing options as well as for hedging them. Though Black-Scholes is the original and most commonly communicated asset pricing model, alternative asset pricing models which incorporate additional …
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Influence Of Target Population Misspecification On Employee Perceptions At A
<p>Numerous researchers have conducted qualitative and quantitative studies examining employee perceptions related to changes in their work environment based upon management/top-down (deductive) communication of vision, mission, and envisioned organization goals Hofstede, Neuijen, Daval, Ohayv, & …
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Examining Uncertainty and Misspecification of Attributes in Cognitive Diagnostic Models
In recent years, cognitive diagnostic models (CDMs) have been widely used in educational assessment to provide a diagnostic profile (mastery/non-mastery) analysis for examinees, which gives insights into learning and teaching. However, there is often uncertainty about the specification of the …
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On the resolution of misspecification in stochastic optimization, variational inequality, and game-theoretic problems
… Nash games complicated by a parametric misspecification, a natural concern in the control of large- scale networked engineered system. In both schemes, players learn the equilibrium strategy while resolving the misspecification: (1) Stochastic Nash games: We present a set of coupled …
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Testing for Structural Change: Evaluation of the Current Methodologies, a Misspecification Testing Perspective and Applications
… Entropy replication algorithm. The proposed misspecification testing procedure relies on resampling techniques to enhance the informational content of the observed data in an attempt to capture heterogeneity 'locally' using rolling window estimators of the primary moments of the stochastic …
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A graphical approach for evaluating the potential impact of bias due to model misspecification in response surface designs
… always faces the potential of bias due to model misspecification. The ultimate impact of this bias depends upon the choice both of the experimental design and of the region for conducting the experiment. This dissertation proposes a graphical approach for evaluating the impact of bias upon …
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When what is wrong seems right: A Monte Carlo simulation investigating the robustness of coefficient omega to model misspecification
… omega-hierarchical under circumstances of model misspecification for high and low reliability measures and different scale lengths. In general, bias increased with the amount of unmodeled complexity (i.e. unspecified multidimensionality or error correlations). When models were misspecified, …
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ESSAYS ON DECISION MAKING UNDER UNCERTAINTY
… be a poor description of the environment (model misspecification). We develop an axiomatic foundation for preferences that capture concerns about these two layers of uncertainty and allow us to compare individuals' degrees of aversion to model misspecification and to ambiguity independently of …
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Q-matrix optimization for cognitive diagnostic assessment
… relationships. However, the Q-matrix is prone to misspecification, given that it is often constructed based solely on human opinions. This thesis uses three research studies to investigate key issues of Q-matrix optimization for cognitive diagnostic assessments. The first study investigates the …
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The Unified Approach for Model Evaluation in Structural Equation Modeling
… misspecified models across all types of misspecification. Furthermore, the rejection rates are negligibly influenced by model characteristics and sample size. The other model evaluation methods do not have all of the desired properties described above. The unified approach, however, does …
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Modifications of Q-learning to Optimize Dynamic Treatment Regimes
… each Q-function. The second challenge is model misspecification. Model misspecification is a common problem in Q-learning, but little attention has been given to its impact when treatment effects are heterogeneous across subjects. We describe the integrative impact of two possible types of model …
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Robust Bayesian inference via optimal transport misfit measures: applications and algorithms
Model misspecification constitutes a major obstacle to reliable inference in many problems. In the Bayesian setting, model misspecification can lead to inconsistency as well as overconfidence in the posterior distribution associated with any quantity of interest, i.e., under-reporting of …
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Essays on Time Series Modeling
… proposed in the literature under different misspecification scenarios: misspecification of the conditional distribution, misspecification of the conditional variance, and misspecification of both the conditional variance and the distribution. Our first main result is that, overall, the …
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Modularized Bayesian Inference: Methodology, Algorithm, Theory And Application.
… data and their generating mechanisms, but misspecification of the model is a major threat to the validity of the inference. Although methods that deal with misspecification have been developed and their properties have been studied, these methods are mainly established based on the premise …
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Fixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysis
… Other simulations assess the effects of model misspecification, and increasing the sample size, either by adding additional studies or by increasing the sizes of a fixed number of studies. Estimates of fixed effects seem insensitive to increasing the sample sizes, but the deviance test of fit …
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Goal alignment: re-analyzing value alignment problems using human-aware AI
… their objective. A foundational cause for misspecification that is being overlooked by the previous works is the inherent asymmetry in human expectations about the agent's behavior and the behavior generated by the agent for the specified objective. To address this, we propose a novel …
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