Global ETD Search
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Showing 1 to 20 of 75 for “"model misspecification"”.
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Sequential design augmentation with model misspecification
… Surface Methodology (RSM) one attempts to model some variable of interest, usually as a known function of design variables. Subsequent analysis often indicates a need to move to a new region of interest. Many times the design is augmented by adding points sequentially to this new region of …
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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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A graphical approach for evaluating the potential impact of bias due to model misspecification in response surface designs
… analysis is to generate a relatively simple model to serve as an adequate approximation for a more complex phenomenon. This model then may be used for other purposes, for example prediction or optimization. Since the proposed model is only an approximation, the analyst almost always faces the …
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When what is wrong seems right: A Monte Carlo simulation investigating the robustness of coefficient omega to model misspecification
Coefficient omega is a model-based reliability estimate that is unrestricted by assumptions of a unidimensional essentially tau equivalent model. Rather, omega can be adapted to suit the underlying factor structure of a given population. A Monte Carlo simulation was used to investigate the …
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ESSAYS ON DECISION MAKING UNDER UNCERTAINTY
… by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. Not only is she uncertain about which model in this set has the best fit (ambiguity), …
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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 …
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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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Applications of optimal portfolio management
… collateral constraints with and without fear of model misspecification. We investigate the properties of the optimal trading strategy, when the investor fully trusts his model dynamics. Subsequently, we investigate how the optimal trading strategy of the investor changes when he mistrusts the …
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Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods
… The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies …
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Enhancing the Reliability of Real-World Evidence and Clinical Decision-Making: Robust, Calibrated, and Uncertainty-Aware Methods for Observational Healthcare Research
… encounter critical challenges, such as model misspecification, systematic biases, and uncertainties in predictions, potentially compromising the reliability and validity of generated evidence. Ensuring reliability is particularly vital for real-world decision-making, where trustworthy …
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Semiparametric Techniques for Response Surface Methodology
… series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they …
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Toward Nonparametric Propensity Score Estimation With Guaranteed Covariate Balance
… on a correctly specified parametric PS </em><em>model. When the model is misspecified, the covariate imbalance may occur, which leads </em><em>to biased estimation of the treatment effect. Therefore, it is necessary to study how to </em><em>improve the model misspecification in propensity score …
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Maximum likelihood estimation of a multivariate log-concave density
… problem. Many methods are either sensitive to model misspecification (parametric models) or difficult to calibrate, especially for multivariate data (nonparametric smoothing methods). We propose an alternative approach using maximum likelihood under a qualitative assumption on the shape of the …
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Statistical Inferences for Two-Component Semiparametric Location-Scale Mixture Models
Mixture models serve as a powerful statistical tool, particularly in capturing heterogeneous populations by representing them as a mixture of several distributions. These models are particularly useful in various fields, including genomics, economics, and social sciences, where data often arises …
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The consequences of ignoring assessment date heterogeneity within waves for latent growth models
In traditional latent growth modeling, researchers assume that assessment dates within waves from longitudinal studies are homogeneous, although they are nearly always heterogeneous. In this study, we present a pedagogical illustration of assessing the consequences of ignoring time-point …
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Data-driven pricing and inventory management with applications in fashion retail
… We are especially interested in demand model misspecification, and show that it can lead to price endogeneity, and hence inconsistent price elasticity estimates and suboptimal pricing decisions. We propose a "random price shock" (RPS) algorithm that combines instrumental variables, well …
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Fixed versus Mixed Parameterization in Logistic Regression Models: Application to Meta-Analysis
Three methods: fixed intercept generalized model (GLM), random intercept generalized mixed model (GLMM), and conditional logistic regression (clogit) are compared in a meta-analysis of 43 studies assessing the effect of diet on cancer incidence in rats. We also perform simulation studies to assess …
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Causal Inference with Survival Outcomes via Orthogonal Statistical Learning
… estimators based on machine learning nuisance models were not available for such outcomes. Thus, researchers wishing to mitigate bias and variance from poor overlap had to accept potential bias from nuisance model misspecification in its place. In Chapter 2, we fill this gap by proposing a …
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Modern challenges in distribution testing
… ** Can we test distributions with tolerance to model misspecification? ** How does the complexity of distribution testing change as we consider different measures of distance? ** Can we efficiently test for membership in (potentially infinite) classes of distributions? ** How can we avoid the …
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