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Erich Lehmann’s Work on Asymptotics
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Erich Lehmann’s contributions to mathematical statistics have been both broad and deep, and his influence on applied statistics has been more widespread than perhaps even he knew. As an example, I…
Adaptively weighted kernel regression
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Robust and efficient regression
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This dissertation aims to address two problems in regression analysis, the model selection and robust parameter estimation in high dimensional linear regressions and developing a robust and efficient estimator in nonparametric regressions.
A comparison of robust estimators based on two types of trimming
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The least trimmed squares (LTS) estimator and the trimmed mean (TM) are two well-known trimming-based estimators of the location parameter. Both estimates are used in practice, and they are…
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A data‐driven weighted linear combination of convex loss functions, together with weighted L1‐penalty is proposed and established a strong oracle property of the method proposed that has both the model selection consistency and estimation efficiency for the true non‐zero coefficients.
Sign-constrained robust least squares, subjective breakdown point and the effect of weights of observations on robustness
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The findings of this paper are summarized as follows: (1) We propose a sign-constrained robust estimation method, which can tolerate 50% of data contamination and meanwhile achieve high,…
On the Use of $L$-functionals in Regression Models
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In this paper we survey and unify a large class or L -functionals of the conditional distribution of the response variable in regression models. This includes robust measures of location, scale,…
Modeling of Chronological Age Using Least Trimmed Squares Ridge Regression
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: The popular method to estimation the parameters of a linear regression model is the ordinary least square method which, despite the sim-plicity of calculating and providing the BLUE estimator of…
Robust M–M unscented Kalman filtering for GPS/IMU navigation
- EngineeringJournal of Geodesy
- 2019
The proposed robust M–M unscented Kalman filter (RMUKF) applies the M-estimation principle to both functional model errors and measurement errors and attenuates the influences of disturbances in the dynamic model and of measurement outliers without linearizing the nonlinear state space model.
Statistical Inference for Big Data
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This dissertation develops novel inferential methods and theory for assessing uncertainty of modern statistical procedures unique to big data analysis and proposes a new inferential framework which addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneity.
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