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 37 for “"Estimation and Inference"”.
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Estimation and Inference for Network Data
… first question we analyze concerns how to understand latent structure in networks. Specifically, we propose a method that estimates the latent type, dimension, and curvature of the latent space model. The second problem we consider concerns network data collection. Collecting full network data is …
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Estimation and inference with nonstationary panel data
… applies the time-series concepts of unit-roots and cointegration to nonstationary panel data. The first three chapters set the scene for what follows and together are the first methodological core of the thesis, on nonstationary panel data estimation and testing.In chapter 1 we consider the …
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Estimation and inference for conditionally heteroscedastic models
… or non-Gaussian errors. For the latter nonstandard case, we use the weighted quantile regression (l$\sb1$) method, gaining both robustness and efficiency, with successful applications to interval forecasting of ARCH type time series models.
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Semidenite representations with applications in estimation and inference
… semidenite optimization-based formulations and approximations for a number of families of optimization problems, including problems arising in spacecraft attitude estimation and in learning tree-structured statistical models. We construct explicit exact reformulations of two families of …
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State estimation and inference in time-varying dynamic systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Essays on set estimation and inference with moment inequalities
This thesis explores power and consistency of estimation and inference procedures with moment inequalities, and applications of the moment inequality framework to estimation of frontiers in finance. In the first chapter, I consider estimation of the identified set and inference on a partially …
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Graphical models for high-dimensional stochastic processes: Estimation and inference
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
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Panel data models with nonadditive unobserved heterogeneity : estimation and inference
This paper considers fixed effects estimation and inference in linear and nonlinear panel data models with random coefficients and endogenous regressors. The quantities of interest - means, variances, and other moments of the random coefficients - are estimated by cross sectional sample moments of …
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Optimal and Safe Semi-supervised Estimation and Inference for High-dimensional Linear Regression
… We refer data consisting of both covariates and the corresponding outcomes as labeled data, and data with only covariates as unlabeled data. Semi-supervised learning combines both labeled and unlabeled data to improve a model using only labeled data and can be useful in these scenarios. In …
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Advances in estimation and inference around a biomarker’s true and false classification rates under trichotomous settings
Discovering and evaluating new biomarkers for the detection of a disease is a topic of globalinterest, especially when dealing with cancer settings. As with many other diseases, cancer has a progressive nature. Therefore, it is not uncommon that in biomarker studies, investigators recruit subjects …
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Model-Based Measures of Interrater Agreement
… subjects as e.g. diseased or not. Nelson and Edwards (2008) propose a generalized linear mixed model for the agreement process, showing that Cohen's κ can seriously underestimate agreement and proposing a model-based coefficient κ<sub>M</sub> which does not suffer from this …
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Unobserved Heterogeneity in Event History Analysis: A Quantile Regression Approach
… history analysis. In Chapter 2 we analyze the estimation and inference procedures of MPH models. In chapter 3 we examine the theoretical properties of random effects duration quantiles, while in Chapter 4 we present a Monte Carlo study of the small sample performance of the aforementioned …
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Essays in Econometrics: Nonparametrics and Robustness
… with implications for applied research, and in each case I attempt to solve that problem. In Chapter 1 I consider the task of inferring causal effects when only `proxy controls' are available. Proxy controls are informative proxies for unobserved confounding factors. For example, suppose …
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Censored Regression Models With Applications to Infrastructure Degradation Studies
… in infrastructure studies, we consider the estimation and inference for regression models where the response variable is bounded or censored. In these conditions, least squares methods are not appropriate, although they are widely used. This dissertation develops a generalization of the …
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Agreement Webs
… decision based on a diagnostic test we propose and study a nonlinear hierarchical model. The model includes a latent propensity-to-disease effect for each item (e.g. patient mammogram, MRI, radiograph, ultra sound, etc) and two effects for each rater, called bias and diagnostic skill. For a …
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Methods and Theory for Nonparametric Inference In High-dimensional Settings
This dissertation addresses nonparametric estimation and inference problems of graphical modeling, linear association assessment, and matrix completion. First, we introduce a flexible framework for nonparametric graphical modeling. We propose three nonparametric measures of conditional dependence, …
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Autoregressive Neural Network Processes - Univariate, Multivariate and Cointegrated Models with Application to the German Automobile Industry
… Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the …
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Joint models for longitudinal and survival data
Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we …
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Approximate Bayesian approaches and semiparametric methods for handling missing data
… consists of four research papers focusing on estimation and inference in missing data. In the first paper (Chapter 2), an approximate Bayesian approach is developed to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the …
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Multiple Imputation of Missing Data in Structural Equation Models with Mediators and Moderators Using Gradient Boosted Machine Learning
<p>Mediation and moderated mediation models are two commonly used models for indirect effects analysis. In practice, missing data is a pervasive problem in structural equation modeling with psychological data. Multiple imputation (MI) is one method used to estimate model parameters in the presence …
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