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 41 for “"nonparametric estimation."”.
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Three essays on nonparametric estimation
… can be viewed as a middle ground between fully nonparametric and fully parametric estimators. For example, typical constraints require the estimator to be \log-concave or, more generally, \rho-concave; (Koenker and Mizera, 2010). In cases in which the true population density satisfies the shape …
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Multivariate nonparametric estimation on censored panel data
… of the duration model. As a consequence, estimation bias is not limited to duration dependence, but extends to the structural parameters as well. They show that a nonparametric mass point approach to marginal likelihood estimation is possible due to Lindsay's characterization of the …
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Sequential nonparametric estimation via Hermite series estimators
… novel approaches to sequential (online) estimation in both stationary and non-stationary settings based on Hermite series density estimators. In the univariate context we apply Hermite series based distribution function estimators to sequential cumulative distribution function estimation. …
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ESSAYS ON NONPARAMETRIC ESTIMATION OF HETEROGENEOUS CAUSAL EFFECTS
My dissertation studies semi- and non-parametric estimation strategies for the distribution of heterogeneous causal effects with applications to labor economics and macroeconomics. \par In the first chapter, I propose a nonparametric strategy to identify the distribution of heterogeneous causal …
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Essays on Nonparametric Estimation of Asset Pricing Models
This thesis studies the use of nonparametric econometric methods to reconcile the empirical behaviour of financial asset prices with theoretical valuation models. The confrontation of economic theory with asset price data requires various functional form assumptions about the preferences and …
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Maximally informative subspaces : nonparametric estimation for dynamical systems
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Essays in semiparametric and nonparametric estimation with application to growth accounting
… dissertation develops efficient semiparametric estimation of parameters and expectations in dynamic nonlinear systems and analyzes the role of environmental factors in productivity growth accounting. The first essay considers the estimation of a general class of dynamic nonlinear systems. The …
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Three Essays on Modeling Financial Risk and Pricing Financial Assets
Essay three: Nonparametric estimation of multifactor diffusion processes and its applications to Heath-Jarrow-Morton interest rate models . This essay develops a nonparametric estimation framework for multifactor diffusion processes with multivariate diffusion functions and applies it to …
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Technical efficiency of Illinois grain farms and factors influencing its measurement
… The effects of applying a parametric versus a nonparametric estimation technique, modeling the frontier as deterministic or stochastic and using a single aggregate-output versus a multiple output measure are examined using a homogeneous sample of Illinois grain farms over three time periods. In …
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Theory and applications of nonparametric regression in item response theory
The simultaneous and nonparametric estimation of latent abilities and item characteristic curves is considered. In particular, the joint asymptotic properties of ordinal ability estimation and kernel smoothed nonparametric item characteristic curve estimation is investigated under relatively …
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Nonparametric Survival Analysis under Shape Restrictions
… associated with a parametric model, we resort to nonparametric methods for estimating a function. Although other nonparametric approaches, such as Kaplan-Meier, kernel-based, and roughness penalty methods, are popular tools for solving function estimation problems, they suffer from some …
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Combinatorial Methods in Statistics
… methods in random vector balancing, nonparametric estimation, and network inference. First, motivated by problems from controlled experiments, we study random vector balancing from the perspective of discrepancy theory, a classical topic in combinatorics, and give sharp statistical …
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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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On the Inductive Biases of Conditional Diffusion Models
… interpolation behavior of neural networks via nonparametric estimation, which happens to have the property of being schedule consistent, or truly denoising at every time step. We find that, assuming a neural network with sufficient capacity, conditional diffusion models are biased towards …
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Statistics for cross-sectional surveys : estimating total time In current state using only elapsed time to date
… survey sample size, probability density function estimation technique, and properties of underlying distributions. We test several nonparametric estimation techniques and report results utilizing Monte Carlo simulations with both discrete and continuous distributions for several types of sampling. …
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The stochastic behavior of spot freight rates and the risk premium in bulk shipping
… rate models are proposed and estimated, applying nonparametric estimation in an effort to avoid misspecification. It is found that while a one-factor Markovian model can capture the mean reversion and level effect in the spot freight rate very well, an extension to a multi-factor non-Markovian …
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A Partly Linear Model for Censored Regression Quantiles
… terms, while at the same time introducing a nonparametric relationship makes them attractive for a number of applications. Their advantage over purely nonparametric models is that they avoid the curse of dimensionality. In quantile regression, examples of partly linear models appear for …
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Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling
… of optimal classifiers, and describe a novel nonparametric estimation of OROC which simultaneous estimates the parameter path of the optimal classifier. In simulations, we compare its performance to alternative methods of OROC estimation. In Chapter 2, we develop a novel method to normalize …
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Topics in functional data analysis and machine learning predictive inference
… <p>The first project deals with the covariance estimation, principal component analysis, and prediction of spatially correlated functional data. We develop a general framework and fully nonparametric estimation methods for spatial functional data collected under a geostatistics setting, where …
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