Search results | J. Murrey Atkins Library

Title: Analysis of failure time data with missing and informative auxiliary covariates.
Author: Ghosh, Lipika
Date Created: 2011
Description: In this dissertation we use Cox's regression model to failure time data with continuous informative auxiliary variables in the presence of a validation subsample. The work is motivated by a common problem of missing or mismeasured covariates in su...

Title: Analysis of failure time data with missing and informative auxiliary covariates.
Author: Ghosh, Lipika
Date Created: 2011
Description: In this dissertation we use Cox's regression model to failure time data with continuous informative auxiliary variables in the presence of a validation subsample. The work is motivated by a common problem of missing or mismeasured covariates in su...

Title: Analysis of failure time data with missing and informative auxiliary covariates.
Author: Ghosh, Lipika
Date Created: 2011
Subjects--Topical: Statistics, Mathematics, Biometry
Description: In this dissertation we use Cox's regression model to failure time data with continuous informative auxiliary variables in the presence of a validation subsample. The work is motivated by a common problem of missing or mismeasured covariates in su...

Title: HIGH CONFIDENCE SET REGULARIZATION IN SPARSE HIGH DIMENSIONAL LOGISTIC REGRESSION WITH MEASUREMENT ERROR
Author: Rao, Maorong
Date Created: 2019
Description: l1 based regularization such as Lasso and Dantizg Selector succeed in two aspects. First,the inherent sparsity of l1 accords with the underlying nature of high dimensional data; second, the convexity essence paves the way to computational feasibil...

Title: HIGH CONFIDENCE SET REGULARIZATION IN SPARSE HIGH DIMENSIONAL LOGISTIC REGRESSION WITH MEASUREMENT ERROR
Author: Rao, Maorong
Date Created: 2019
Subjects--Topical: Mathematics
Description: l1 based regularization such as Lasso and Dantizg Selector succeed in two aspects. First,the inherent sparsity of l1 accords with the underlying nature of high dimensional data; second, the convexity essence paves the way to computational feasibil...

Title: Independent Screening for Nonparametric Additive Cox Model
Author: Yu, Sha
Date Created: 2020
Description: Survival data with ultrahigh dimensional covariates are increasingly common recently due to the rapid development in technologies. It is challenging to model them using survival models in order to understand the association between covariate infor...

Title: Independent Screening for Nonparametric Additive Cox Model
Author: Yu, Sha
Date Created: 2020
Subjects--Topical: Statistics
Description: Survival data with ultrahigh dimensional covariates are increasingly common recently due to the rapid development in technologies. It is challenging to model them using survival models in order to understand the association between covariate infor...

Title: Interval Estimation for Semiparametric Predictive Regression
Author: Hong, Shaoxin
Date Created: 2018
Description: Predictive regression is an important research topic in financial econometrics. Various estimation methods have been proposed for it, but they suffer from complicated asymptotic limits which depend on whether or not the predicting variable is stat...

Title: Interval Estimation for Semiparametric Predictive Regression
Author: Hong, Shaoxin
Date Created: 2018
Description: Predictive regression is an important research topic in financial econometrics. Various estimation methods have been proposed for it, but they suffer from complicated asymptotic limits which depend on whether or not the predicting variable is stat...

Title: Interval Estimation for Semiparametric Predictive Regression
Author: Hong, Shaoxin
Date Created: 2018
Subjects--Topical: Statistics
Description: Predictive regression is an important research topic in financial econometrics. Various estimation methods have been proposed for it, but they suffer from complicated asymptotic limits which depend on whether or not the predicting variable is stat...

Title: LOCAL SPATIAL QUANTILE ESTIMATION OF MULTIVARIATE FUNCTIONAL-COEFFICIENT REGRESSION MODELS
Author: Zhou, Yetong
Date Created: 2020
Description: Quantile regression (QR) has been widely studied in statistics and econometrics. However, there is no much work on nonlinear QR for vector time series. Therefore, we propose a local spatial QR method to estimate the functional-coefficient matrices...

Title: LOCAL SPATIAL QUANTILE ESTIMATION OF MULTIVARIATE FUNCTIONAL-COEFFICIENT REGRESSION MODELS
Author: Zhou, Yetong
Date Created: 2020
Subjects--Topical: Statistics
Description: Quantile regression (QR) has been widely studied in statistics and econometrics. However, there is no much work on nonlinear QR for vector time series. Therefore, we propose a local spatial QR method to estimate the functional-coefficient matrices...

Title: Modeling Vector Time Series Data
Author: Liu, Yi
Date Created: 2013
Description: ABSTRACTIn this dissertation, firstly, I study spatial quantile regression estimation of multivariate threshold time series models. Asymptotic normality of the proposed spatial quantile regression estimator is established. Simulations and a real e...

Title: Modeling Vector Time Series Data
Author: Liu, Yi
Date Created: 2013
Subjects--Topical: Statistics
Description: ABSTRACTIn this dissertation, firstly, I study spatial quantile regression estimation of multivariate threshold time series models. Asymptotic normality of the proposed spatial quantile regression estimator is established. Simulations and a real e...

Title: NONPARAMETRIC PREDICTIVE REGRESSION
Author: Yu, Xintian
Date Created: 2016
Description: In ﬁnancial time series nonlinear eﬀects and time-varying eﬀects are observed. In this dissertation we propose a predictive regression model with time varying coeﬃcients and functional coeﬃcients. It allows for nonstationary predictors. We establi...

Title: NONPARAMETRIC PREDICTIVE REGRESSION
Author: Yu, Xintian
Date Created: 2016
Subjects--Topical: Statistics, Finance, Economics
Description: In ﬁnancial time series nonlinear eﬀects and time-varying eﬀects are observed. In this dissertation we propose a predictive regression model with time varying coeﬃcients and functional coeﬃcients. It allows for nonstationary predictors. We establi...

Title: Non-nested Model Selection via Empirical Likelihood
Author: Zhao, Cong
Date Created: 2017
Description: In this dissertation we propose an empirical likelihood ratio (ELR) test to conductnon-nested model selection. It allows for heteroscedasticity and works for any twosupervised statistical learning methods under mild conditions. We establish asympt...

Title: Non-nested Model Selection via Empirical Likelihood
Author: Zhao, Cong
Date Created: 2017
Subjects--Topical: Statistics
Description: In this dissertation we propose an empirical likelihood ratio (ELR) test to conductnon-nested model selection. It allows for heteroscedasticity and works for any twosupervised statistical learning methods under mild conditions. We establish asympt...

Title: ROBUST GENERALIZED LIKELIHOOD RATIO TEST BASED ON PENALIZATION
Author: Zhang, Meijiao
Date Created: 2018
Description: The Least absolute deviation combined with the Least absolute shrinkage andselection operator (LAD-LASSO) estimator can do regression shrinkage and selectionand is also resistant to outliers or heavy-tailed errors which is proposed in Wang etal. (...

Title: ROBUST GENERALIZED LIKELIHOOD RATIO TEST BASED ON PENALIZATION
Author: Zhang, Meijiao
Date Created: 2018
Subjects--Topical: Statistics
Description: The Least absolute deviation combined with the Least absolute shrinkage andselection operator (LAD-LASSO) estimator can do regression shrinkage and selectionand is also resistant to outliers or heavy-tailed errors which is proposed in Wang etal. (...