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 24 for “"robust estimators"”.
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A Comparative Simulation Study of Robust Estimators of Standard Errors
… the covariance structure. Another approach is robust estimation. In this approach, the covariance structure is estimated from the data. White (1980) introduced a biased, but consistent, robust estimator. Long et al. (2000) added an adjustment factor to White's estimator to remove the bias of …
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Extending linear grouping analysis and robust estimators for very large data sets
… optimization problems in the field of robust statistics, and demonstrate, via simulation study as well as application on actual data sets, that the BIRCH solution compares favourably to the existing state-of-the-art alternatives, and in many cases finds a more optimal solution.
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Essays On Robust Estimators For Non-Identically Distributed Observations In Spatial Econometric And Time Series Models
… The first essay discusses the heteroskedasticity robust generalized method of moments estimator (RGMME) for the spatial models that allow for spatial dependence in both the dependent variable and the disturbance term (SARAR(1,1)). First, we show that the maximum likelihood estimator (MLE) is …
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Investigating the performance of process-observation-error-estimator and robust estimators in surplus production model: a simulation study
… study investigated the performance of the three estimators of surplus production model including process-observation-error-estimator with normal distribution (POE_N), observation-error-estimator with normal distribution (OE_N), and process-error-estimator with normal distribution (PE_N). The …
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Numerical Contributions to the Asymptotic Theory of Robustness
… software R has been developed. It includes all robust procedures introduced throughout the thesis. The dissertation itself consists of five parts and starts with a brief motivation, which makes precise why robust statistics is necessary. After that a detailed summary in German and English is …
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A robust optimization approach to statistical estimation problems by Apostolos G. Fertis.
… have long been intuitive connections between robustness and regularization in statistical estimation, for example, in lasso and support vector machines. In the first part of the thesis, we formalize these connections using robust optimization. Specifically (a) We show that in classical …
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On robust jump detection in regression surfaces with applications to image analysis
… case the difference of two one-sided kernel estimators can be used to detect discontinuities in regression functions. In smooth regions, an estimator using only observations on the left side will be similar to the estimator using only observations on the right side. In contrast, near jump …
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Fault detection in multivariate processes : handling autocorrelation, contamination, and small sample sizes in engineered systems.
… and finally, a novel Bayesian approach using robust estimators is proposed to handle situations in which historical data are both limited and contaminated with outliers.
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Essays On Spatial Econometrics: Estimation Methods And Applications
… the spatial weight matrices. Then, we extend the robust generalized method of moment (GMM) estimation approach in Lin and Lee (2010) for the spatial models allowing for a spatial lag not only in the dependent variable but also in the disturbance term. We show the consistency of the robust GMM …
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Robust Blind Spectral Estimation in the Presence of Impulsive Noise
Robust nonparametric spectral estimation includes generating an accurate estimate of the Power Spectral Density (PSD) for a given set of data while trying to minimize the bias due to data outliers. Robust nonparametric spectral estimation is applied in the domain of electrical communications and …
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Robust GM Wiener Filter in the Complex Domain
… early STAP radar systems is the reliance on non-robust estimators to estimate the noise condition. When even a single outlier is present, the earliest STAP radar systems would break down, causing the target to be missed. Many algorithms have been developed to successfully estimate the noise …
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Thin trading, non-normality and the estimation of systematic risk on small stock markets
… is to offer concrete suggestions for selecting estimators of beta coefficients. In order to attain the objective outlined above, the first steps are to establish the extent and to model the characteristics of thin trading and non-normality. This is achieved in the thesis with the aid of …
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Some problems in the analysis of spatial pattern
… and hypothesis testing of this histogram.Two new robust estimators of the density of a forest stand are described, which are unbiased for a wide range of spatial patterns. The first estimator has a coefficient which varies according to some quantitative feature of the spatial pattern. This is a …
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Causal Inference Methods for Estimation of Survival and General Health Status Measures of Alzheimer’s Disease Patients
… a thorough causal inference study using doubly robust estimators, nonparametric bootstrap confidence intervals, Bonferroni corrections for multiple comparisons and analyzing one of the largest high-quality medical databases containing millions of de-identified electronic health records obtained …
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Applications of Causal Inference Methods for the Estimation of Effects of Bone Marrow Transplant and Prescription Drugs on Survival of Aplastic Anemia Patients
… techniques to navigate complex data. Utilizing robust, clinically significant methods such as doubly robust estimators, our research reveals a marked improvement in survival rates for BMT recipients, particularly adults, over one year. The studies also recognize the typically more severe stages …
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Highly Robust and Efficient Estimators of Multivariate Location and Covariance with Applications to Array Processing and Financial Portfolio Optimization
… by outliers or impulsive noise. To address this, robust estimators should be employed. However, in signal processing, where complex-valued data are common, the robust estimation techniques currently employed, such as M-estimators, provide limited robustness in the multivariate case. For this …
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Essays on misspecified models
… properties of generalized empirical likelihood estimators when moment conditions are not correctly specified. Classical generalized empirical likelihood estimators rely on the correct moment conditions, however, those conditions are mostly generated from economic theory and some of them are not …
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Robust GMSK Demodulation Using Demodulator Diversity and BER Estimation
This research investigates robust demodulation of Gaussian Minimum Shift Keying (GMSK) signals, using demodulator diversity and real-time bit-error-rate (BER) estimation. GMSK is particularly important because of its use in promi- nent wireless standards around the world (GSM, DECT, CDPD, DCS1800, …
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A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control
… on the availability of fast, accurate, and robust dynamic state estimators. These estimators should be robust to gross errors on the measurements and the model parameter values while providing good state estimates even in the presence of large dynamical system model uncertainties and …
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Parametric Estimation of the Heston Model under the Indirect Observability Framework
… lead to the question about the consistency and robustness of statistical estimation of parameters in the chosen stochastic model. Three parts are presented in this dissertation. In part I (Chapter 2) of this dissertation we show that the Method of Moments can be used to derive consistent and …
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