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Showing 1 to 20 of 39 for “"robust regression"”.
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Dual Model Robust Regression
In typical normal theory 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 …
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Linear Mixed Model Robust Regression
… for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved …
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Outlier Resistant Model Robust Regression
Parametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this …
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Asymptotic Results for Model Robust Regression
… esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, ƒ, and the nonparametric function, 𝑔, were combined in a straightforward fashion via the use …
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Robust regression methods for insurance risk classification
… statistical methods. It is known that logistic regression is among the many sophisticated statistical methods used by the banking industry in order to select credit rating variables. Extending the method to insurance risks seems only natural. Insurance risks are not usually classified in only …
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Robust Regression Methods For Massively Decayed Intelligence Data
… </p> <p>Using Monte Carlo methods, three regression techniques (ordinary least squares, least-trimmed, and maximum likelihood) were tested against different data decay models presumed to be found in homeland security research studies in order to test whether these techniques will preserve …
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Model Robust Regression Based on Generalized Estimating Equations
One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a …
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Model robust regression: combining parametric, nonparametric, and semiparametric methods
In obtaining a regression fit to a set of data, ordinary least squares regression depends directly on the parametric model formulated by the researcher. If this model is incorrect, a least squares analysis may be misleading. Alternatively, nonparametric regression (kernel or local polynomial …
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Examination and Comparison of the Performance of Common Non-Parametric and Robust Regression Models
… of the Performance of Common Non-Parametric and Robust Regression Models</p> <p>By</p> <p>Gregory Frank Malek</p> <p>Stephen F. Austin State University, Masters in Statistics Program,</p> <p>Nacogdoches, Texas, U.S.A.</p> <p><a href="mailto:g_m_2002@live.com">g_m_2002@live.com</a></p> <p>This …
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Scalable Robust Models Under Adversarial Data Corruption
… the fast-growing amount of data makes the robust models more difficult to handle large-scale data sets. This thesis focuses on the development of methods for scalable robust models under the adversarial data corruption assumptions. Four methods are proposed, including robust regression via …
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Robust beta estimation and applications
… be estimated using Ordinary Least Squares (OLS) regression. More recent empirical evidence has however cast doubt over the assumption of normality and has suggested that market returns tend to be non-normal. If the assumption of normality is no longer upheld then OLS or Maximum Likelihood …
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Monte Carlo simulation with parametric and nonparametric analysis of covariance for nonequivalent control groups
… parent population parameters, and results of robust regression. Results of data exploration on the parent population revealed that, based on assumptions, the covariate-corrected ANCOVAS are preferred over both the parametric and rank analyses. A reliability coefficient of ṟ=.83 also indicated …
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The Dynamics Of Compression On Presidential Nominations
… compiled from FEC candidate filling, I use a robust regression model to demonstrate a correlation between state movement of contests and candidate disbursements in that state. Next I explore the impact of compression on candidate entry into the nomination race using a log-logistical model of …
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Inter-temporal Effect of Technological Capabilities on Firm Performance: a Longitudinal Study of the U.s. Computed Tomography Industry (1972-2002)
… inception, I find support for the theory. Using robust regression and multinomial logistic regression models, I find that as technology in an industry advances, technological capabilities become weaker drivers of firm performance. I discuss the shortcomings of this research and potential for …
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Robust estimation, regression and ranking with applications in portfolio optimization
… are only approximations of reality, many robust statistical methods have been developed to produce estimators that are robust against the deviation from the model assumptions. Unfortunately, these techniques have very high computational complexity that prevents their application to large …
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Robust Analysis of M-Estimators of Nonlinear Models
… and normality. We focus on analyzing the robustness properties of M-estimators of nonlinear models by studying the effects of deviations from assumptions and normality on these estimators. We discuss St. Laurent and Cook's Jacobian Leverage and identify the relationship of the technique to …
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Performance Analysis of an Urban Stormwater Best Management Practice Retrofit
… Probability Method. The Kaplan-Meier method and Robust Regression on ordered statistics (ROS) were used to make it possible to include censored datasets in the analysis. Analysis with the SOL method showed removal of suspended sediment, nitrogen, iron, and copper. Export of dissolved solids, …
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Statistical Modeling of Simulation Errors and Their Reduction via Response Surface Techniques
… produced inaccurate RS approximations. A robust regression technique, M-estimation implemented by iteratively reweighted least squares (IRLS), is used to identify the outliers, which are then repaired by higher fidelity optimizations. The IRLS procedure is applied to the results of the …
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The Feedback-Related Negativity is a Time-Dependent Brain Mechanism that Facilitates Aversive Learning: Implications for the Reinforcement Learning FRN Hypothesis
… analyses were supplemented by single-trial robust regression of feedback-locked EEG. The feedback-related negativity (FRN), a mediofrontal ERP component thought to convey a PE signal, showed robust changes in activation over time but did not respond to trial-by-trial magnitude of prediction …
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Semi-Parametric Techniques for Multi-Response Optimization
… The parametric approach, a traditional RSM regression technique, which is inflexible and heavily relies on the assumption of well-estimated models for the response of interests, can lead to highly biased estimates and result in miscalculating optimal solutions when the user's model is …
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