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 30 for “"Variance estimation"”.
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Investigating 'optimal' kriging variance estimation :analytic and bootstrap estimators
… error (MSPE), commonly termed the kriging variance.
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An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks
… threshold energy detection model with noise variance estimation for implementation in CRSN systems. Experimental work on our adaptive threshold technique based on the recursive one-sided hypothesis test (ROHT) technique was carried out using MatLab. The results obtained indicate that our …
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Consistency and Uniform Bounds for Heteroscedastic Simulation Metamodeling and Their Applications
… whose output is subject to input-dependent noise variance. Several challenges remain unsolved in this field. First, in-depth investigations into the consistency of heteroscedastic metamodeling techniques, particularly from the sequential prediction perspective, are lacking. Second, sequential …
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Statistical inference for residual time quantiles in regression models for censored time-to-event data
… model. In each setting, we consider point estimation, asymptotic properties, variance estimation, confidence interval construction, and inference. We also perform simulations to demonstrate our estimators' performance and provide examples of their application to sample data sets. We finish …
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A two-stage experimental design procedure under dispersion effects
Under heterogeneous variance, conventional optimal response surface experimental designs for estimating location models are no longer optimal. To address this deficiency. D and Q criteria appropriate under heterogeneous variance are developed. These criteria are then applied to demonstrate the …
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Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis
… of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. A series of Monte-Carlo simulations were conducted to investigate the …
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The Influence of Task Process and Task Structure on Transactive Memory Systems and Team Outcomes: A Meta-Regression Analysis
… effects for all team outcomes through a robust variance estimation, <em>z </em>= 0.47 (<em>r</em> = 0.44), 95% CI [0.38, 0.56], p < .001. Exploratory meta-regression results revealed a significant difference in outcome measurement type, with self-report surveys (<em>z </em>= 0.78, r = 0.65, 95% …
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Power approximation for the test of study-level categorical moderators in meta-regression with dependent effect sizes
… effects model with robust variance estimation (CHE+RVE). Additionally, I conduct a Monte Carlo simulation to validate this power approximation formula against the true simulated power of a test of multiple contrasts from a CHE+RVE model. I also examine the Type I error rates …
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Response surface designs and analysis for bi-randomization error structures
… to robust parameter design, process mean and variance models are formulated to aid in designing products to be "robust" to uncontrollable system influences called noise variables. In model development, noise variables are assumed to be controllable in the laboratory, but due to their random …
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Some Sampling Designs and Estimation Problems
… by Cordy (1993), we consider design-based variance estimation for probability sampling from a continuous and spatially distributed universe. Using this theory in chapter two, the sampling design of one random point from each cell of a translated grid is investigated and the problem of edge …
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Estimation and Testing Methods for Monotone Transformation Models
… models, resulting in simultaneous point and variance estimations. The self-induced smoothing does not require bandwidth selection, yet provides the right amount of smoothness so that the estimator is asymptotically normal with mean zero (unbiased) and variance-covariance matrix consistently …
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Improved robustness formulations and a simulation-based robust concept exploration method
… response surfaces and the Taylor series for variance estimations. In order to analyze the limitation of the robustness estimation, several alternative methods are developed, assessed and introduced to a modified RCEM. The developed Multiple Point Method is based on the Sensitivity Index (SI) …
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Dual Model Robust Regression
… regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin (1987) proposes a …
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Statistical issues and developments in time series analysis and educational measurement
… a consistent estimator but involves consistent estimation of the variance of the normalized sample mean using one seemingly arbitrary tuning parameter. By adopting a self-normalization idea, we modify the subsampling procedure of Hall et al.(1998) and the resulting procedure does not require …
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Simulating Dynamical Systems from Data
… we propose and analyze algorithms for mean and variance estimation and forecasting of time series with varying noise models, data missingness patterns, and assumptions on the factorization structure. These algorithms employ variants of the classical multivariate singular spectrum analysis (mSSA) …
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A Model-Based Approach to Demodulation of Co-Channel MSK Signals
… in the linear model form, hence a minimum variance unbiased (MVU) estimator exists that satisfies the Cramer-Rao lower bound (CRLB) with equality. This framework allows us to derive the best estimators for a single-user and a two-user case. These concepts can also be extended to wideband …
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Statistical methods in a high school transcript survey
… designed to produce specific accuracy of direct estimation at high levels of aggregation. Sample sizes for small geographical areas or subpopulations are typically small such that direct estimates in these areas are very unreliable.</p> <p>Particularly in designs where a single primary sampling …
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New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles
… we first investigate the use of automated variance estimators in distribution-free statistical process control (SPC) charts for univariate autocorrelated processes. We introduce two variance estimators---the standardized time series overlapping area estimator and the so-called …
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Statistical uncertainty quantification for machine learning models and training acceleration for graph neural networks
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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