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 9 of 9 for “"Constrained Estimation"”.
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Measurement covariance-constrained estimation for poorly modeled dynamic systems
An optimal estimation strategy is developed for post-experiment estimation of discretely measured dynamic systems which accounts for system model errors in a much more rigorous manner than Kalman filter-smoother type methods. The Kalman filter-smoother type methods, which currently dominate …
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Lp norm estimation procedures and an L1 norm algorithm for unconstrained and constrained estimation for linear models
… attention of late is minimum L<sub>p</sub> norm estimation. However, the statistical efüciency of a L<sub>p</sub> estimator depends greatly on the underlying distribution of errors and on the value of p. Thus, the choice of an appropriate value of p is crucial to the effectiveness of <sub>p</sub> …
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Minimax estimation with structured data : shape constraints, causal models, and optimal transport
… used to overcome these difficulties in several estimation problems, spanning three different areas of statistics: shape-constrained estimation, causal discovery, and optimal transport. In the area of shape-constrained estimation, we study the estimation of matrices, first under the assumption of …
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The LASSO linear mixed model for mapping quantitative trait loci
… of observations. This can be overcome by using constrained estimation methods or by making the marker effects random. One method of constrained estimation is the least absolute selection and shrinkage operator (LASSO). This method has the appealing ability to produce predictions of effects that …
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Synergistic Modeling of Advanced Manufacturing Processes with Functional Variables
… model. A hierarchical non-negative garrote constrained estimation method is proposed. Second, the quality-process relationships for scalar offline setting variables, functional in situ process variables, and manufacturing quality responses are studied in Chapter 3. A functional graphical …
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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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Matrix estimation with latent permutations
… This problem is at the intersection of shape constrained estimation which has a long history in statistics, and latent permutation learning which has driven a recent surge of interest in the machine learning community. Shape constraints on matrices, such as monotonicity and smoothness, are …
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State Estimation with Unconventional and Networked Measurements
… consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State …
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Simultaneous state and input estimation with applications in vehicle problems
… there is a well developed theory of optimal estimation and control, research on systems with inputs that are not measured directly is still ongoing. Motivated by automotive applications where it is not always feasible or practical to have sensors that measure all vehicle inputs, we aim to …