Works (52)

Updated: August 13th, 2023 21:17

2022 article

Automatic structure recovery for generalized additive models

Shen, K., & Wu, Y. (2022, October 18). CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE.

By: K. Shen n & Y. Wu n

author keywords: Backfitting; bandwidth estimation; local polynomial smoothing; profiling; variable selection
TL;DR: This article proposes an automatic structure recovery method for generalized additive models (GAMs) by extending Wu and Stefanski's approach based on a local scoring algorithm coupled with local polynomial smoothing, along with a kernel‐based variable selection approach. (via Semantic Scholar)
Source: Web Of Science
Added: October 31, 2022

2020 journal article

Nonparametric Estimation of Multivariate Mixtures

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(531), 1456–1471.

By: C. Zheng n & Y. Wu*

author keywords: Density estimation; Nonparametric mixture model; Tensor
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 12, 2019

2020 journal article

Tuning parameter selection for penalised empirical likelihood with a diverging number of parameters

JOURNAL OF NONPARAMETRIC STATISTICS, 32(1), 246–261.

By: C. Zheng n & Y. Wu*

author keywords: Tuning parameter selection; variable selection; generalised information criterion; empirical likelihood
TL;DR: A generalised information criterion (GIC) for the penalised empirical likelihood in the linear regression case is proposed and it is shown that the tuning parameter selected by the GIC yields the true model consistently even when the number of predictors diverges to infinity with the sample size. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: April 20, 2020

2018 journal article

FULLY EFFICIENT ROBUST ESTIMATION, OUTLIER DETECTION AND VARIABLE SELECTION VIA PENALIZED REGRESSION

STATISTICA SINICA, 28(2), 1031–1052.

By: D. Kong*, H. Bondell* & Y. Wu*

author keywords: Adaptive; breakdown point; least trimmed squares; outliers; penalized regression; robust regression; variable selection
TL;DR: An e-cient algorithm is proposed to solve this jointly penalized optimization problem and use the extended Bayesian information criteria tuning method to select the regularization parameters, since the number of parameters exceeds the sample size. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: December 3, 2018

2018 journal article

Principal quantile regression for sufficient dimension reduction with heteroscedasticity

ELECTRONIC JOURNAL OF STATISTICS, 12(2), 2114–2140.

By: C. Wang*, S. Shin* & Y. Wu*

author keywords: Heteroscedasticity; kernel quantile regression; principal quantile regression; sufficient dimension reduction
TL;DR: A new SDR method called principal quantile regression (PQR) is proposed that efficiently and competitively tackles heteroscedasticity and can naturally be extended to a nonlinear version via kernel trick. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: March 25, 2019

2017 journal article

Laccase-immobilized bacterial cellulose/TiO2 functionalized composite membranes: Evaluation for photo- and bio-catalytic dye degradation

Journal of Membrane Science, 525, 89–98.

By: G. Li*, A. Nandgaonkar n, Q. Wang*, J. Zhang*, W. Krause n, Q. Wei*, L. Lucia n

author keywords: Laccase; Bacterial cellulose; Titanium dioxide; Immobilization; Dye degradation
UN Sustainable Development Goal Categories
Sources: Web Of Science, Crossref, NC State University Libraries
Added: August 6, 2018

2017 journal article

Principal weighted support vector machines for sufficient dimension reduction in binary classification

Biometrika, 104(1), 67–81.

By: S. Shin, Y. Wu, H. Zhang & Y. Liu

Source: NC State University Libraries
Added: August 6, 2018

2017 journal article

The robust EM-type algorithms for log-concave mixtures of regression models

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 111, 14–26.

By: H. Hu n, W. Yao* & Y. Wu n

author keywords: EM algorithm; Log-concave Maximum Likelihood Estimator; Mixture of regression model; Robust regression
TL;DR: A new method is proposed to estimate the mixture regression parameters by only assuming that the components have log-concave error densities but the specific parametric family is unknown, which has comparable performance to the normal EM algorithm. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2016 journal article

A consistent information criterion for support vector machines in diverging model spaces

Journal of Machine Learning Research, 17.

By: X. Zhang, Y. Wu, L. Wang & R. Li

Source: NC State University Libraries
Added: August 6, 2018

2016 journal article

An Error bound for l-1-norm support vector machine coefficients in ultra-high dimension

Journal of Machine Learning Research, 17.

By: B. Peng, L. Wang & Y. Wu

Source: NC State University Libraries
Added: August 6, 2018

2016 journal article

LOCAL INDEPENDENCE FEATURE SCREENING FOR NONPARAMETRIC AND SEMIPARAMETRIC MODELS BY MARGINAL EMPIRICAL LIKELIHOOD

ANNALS OF STATISTICS, 44(2), 515–539.

By: J. Chang n, C. Tang n & Y. Wu*

author keywords: Empirical likelihood; high-dimensional data analysis; nonparametric and semiparametric models; sure independence screening
TL;DR: This work considers an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis that accommodates a wide spectrum of nonparametric and semiparametric model families. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2016 journal article

Maximum likelihood estimation of the mixture of log-concave densities

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 101, 137–147.

By: H. Hu n, Y. Wu n & W. Yao*

author keywords: Consistency; Log-concave maximum likelihood estimator (LCMLE); Mixture model
TL;DR: This paper considers a much more flexible mixture model, which assumes each component density to be log-concave, and shows that the LCMLE improves the clustering results while comparing with the traditional MLE for parametric mixture models. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2016 journal article

On quantile regression in reproducing kernel hilbert spaces with the data sparsity constraint

Journal of Machine Learning Research, 17.

By: C. Zhang, Y. Liu & Y. Wu

Source: NC State University Libraries
Added: August 6, 2018

2016 journal article

Probability-enhanced effective dimension reduction for classifying sparse functional data

TEST, 25(1), 1–22.

By: F. Yao, Y. Wu & J. Zou

Source: NC State University Libraries
Added: August 6, 2018

2016 article

Rejoinder on: Probability enhanced effective dimension reduction for classifying sparse functional data

Yao, F., Wu, Y., & Zou, J. (2016, March). TEST, Vol. 25, pp. 52–58.

By: F. Yao*, Y. Wu n & J. Zou*

TL;DR: The views about the proposed work’s major contributions to the area of sparse functional data classification are expressed and some possible future research directions are suggested. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2016 journal article

Variable selection for support vector machines in moderately high dimensions

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 78(1), 53–76.

By: X. Zhang n, Y. Wu n, L. Wang* & R. Li*

author keywords: Local linear approximation; Non-convex penalty; Oracle property; Support vector machines; Ultrahigh dimensions; Variable selection
TL;DR: It is proved that, in ultrahigh dimensions, there is one local minimizer to the objective function of non‐convex penalized SVMs having the desired oracle property and the local linear approximation algorithm is guaranteed to converge to the oracle estimator even in the ultrahigh dimensional setting if an appropriate initial estimator is available. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

Automatic structure recovery for additive models

BIOMETRIKA, 102(2), 381–395.

By: Y. Wu n & L. Stefanski n

author keywords: Backfitting; Bandwidth estimation; Kernel; Local polynomial; Measurement-error model selection likelihood; Model selection; Profiling; Smoothing; Variable selection
TL;DR: An automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy is proposed, and an extension to partially linear models is described. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

ConvexLAR: An Extension of Least Angle Regression

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 24(3), 603–626.

By: W. Xiao*, Y. Wu* & H. Zhou*

author keywords: Solution path; Group lasso; Regularization; Ordinary differential equation (ODE); Lasso
TL;DR: This article proposes a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection and yields new exact path algorithms for certain penalty methods such as a conveX loss function with lasso or group lasso penalty. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

Domain selection for the varying coefficient model via local polynomial regression

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 83, 236–250.

By: D. Kong n, H. Bondell n & Y. Wu n

author keywords: Bandwidth selection; Oracle properties; Penalized local polynomial fitting; SCAD
TL;DR: The estimators enjoy the oracle properties in the sense that they have the same bias and asymptotic variance as the local polynomial estimators as if the sparsity is known as a priori. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

Effective dimension reduction for sparse functional data

BIOMETRIKA, 102(2), 421–437.

By: F. Yao*, E. Lei* & Y. Wu n

author keywords: Cumulative slicing; Effective dimension reduction; Inverse regression; Sparse functional data
TL;DR: The theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

Homogeneity Pursuit

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 110(509), 175–194.

By: Z. Ke*, J. Fan* & Y. Wu n

author keywords: Clustering; Sparsity
Source: Web Of Science
Added: August 6, 2018

2015 journal article

IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 110(511), 975–986.

By: W. Sun, Y. Liu, J. Crowley, T. Chen, H. Zhou*, H. Chu, S. Huang, P. Kuan ...

author keywords: Differential isoform expression; Differential isoform usage; Isoform; Penalized regression; RNA-seq
TL;DR: A statistical method named IsoDOT is developed to assess differential isoform expression (DIE) and differentialisoform usage (DIU) using RNA-seq data and identifies a group of genes whose isoform usages respond to haloperidol treatment. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2015 journal article

Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood

JOURNAL OF NONPARAMETRIC STATISTICS, 27(2), 195–213.

By: C. Davenport n, A. Maity n & Y. Wu n

Contributors: C. Davenport n, A. Maity n & Y. Wu n

author keywords: nonparametric regression; varying coefficient model; generalised linear models; local polynomial smoothing; parametrically guided estimation; 62G08; 62J12
TL;DR: A guided estimation procedure for the nonparametric VCMs is developed and asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2014 journal article

A Generic Path Algorithm for Regularized Statistical Estimation

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 109(506), 686–699.

By: H. Zhou n & Y. Wu n

author keywords: Gaussian graphical model; Generalized linear model; Lasso; Log-concave density estimation; Ordinary differential equations; Quasi-likelihoods; Regularization; Shape restricted regression; Solution path
TL;DR: This article proposes an exact path solver based on ordinary differential equations (EPSODE) that works for any convex loss function and can deal with generalized ℓ1 penalties as well as more complicated regularization such as inequality constraints encountered in shape-restricted regressions and nonparametric density estimation. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2014 journal article

RKHS-based functional nonparametric regression for sparse and irregular longitudinal data

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 42(2), 204–216.

By: M. Avery*, Y. Wu n, H. Helen Zhang* & J. Zhang*

author keywords: Functional nonparametric regression; longitudinal data; RKHS; sparse and irregular
TL;DR: A new functional nonparametric regression framework based on reproducing kernel Hilbert spaces (RKHS) is proposed, which shows improvement over existing methods in simulation studies as well as in a real data example. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2014 journal article

Two-dimensional solution surface for weighted support vector machines

Journal of Computational and Graphical Statistics, 23(2), 383–402.

By: S. Shin n, Y. Wu n & H. Zhang*

TL;DR: This article establishes that the WSVM solutions are jointly piecewise-linear with respect to both the regularization and weight parameter and develops a state-of-the-art algorithm that can compute the entire trajectory of theWSVM solutions for every pair of the regularized parameter and the weight parameter at a feasible computational cost. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

2014 journal article

Variable selection for sparse high-dimensional nonlinear regression models by combining nonnegative garrote and sure independence screening

Statistica Sinica, 24(3), 1365–1387.

By: S. Wu, H. Xue, Y. Wu & H. Wu

Source: NC State University Libraries
Added: August 6, 2018

2014 article

Variable selection in large margin classifier-based probability estimation with high-dimensional predictors

Shin, S. J., & Wu, Y. (2014, July). BIOMETRICAL JOURNAL, Vol. 56, pp. 594–596.

By: S. Shin* & Y. Wu n

author keywords: Max-type penalty; Regularization; Variable selection
MeSH headings : Artificial Intelligence; Probability; Support Vector Machine
TL;DR: This is a discussion of the papers: “Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory and Applications” by Jochen Kruppa, Yufeng Liu, Hans‐Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2013 journal article

Adaptively Weighted Large Margin Classifiers

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 22(2), 416–432.

By: Y. Wu* & Y. Liu*

author keywords: Binary classification; Data adaptive learning; Multicategory classification; SVM; Weighted learning
TL;DR: A new weighted large margin classification technique is proposed that is robust to outliers and thus is able to produce more accurate classification results. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2013 journal article

Continuously additive models for nonlinear functional regression

BIOMETRIKA, 100(3), 607–622.

By: H. Mueller, Y. Wu* & F. Yao

author keywords: Berkeley growth study; Functional data analysis; Functional regression; Gene expression; Generalized response; Stochastic process; Tensor spline
Source: Web Of Science
Added: August 6, 2018

2013 journal article

Coordinate great circle descent algorithm with application to single-index models

Statistics and Its Interface, 6(4), 511–518.

By: P. Zeng* & Y. Wu n

TL;DR: A novel coordinate great circle descent algorithm is proposed to solve family of optimization problems with a unit-norm constraint and the validity of the algorithm is justified both theoretically and via simulation studies. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

2013 journal article

Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 22(2), 379–395.

By: Y. Wu n & Y. Liu*

author keywords: Classification; Functional principal component analysis; Multicategory; Reproducing kernel Hilbert space; SVM; Truncated-hinge-loss SVM
TL;DR: This article proposes functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis on sparse and irregular longitudinal data with a multicategory response. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2013 journal article

MARGINAL EMPIRICAL LIKELIHOOD AND SURE INDEPENDENCE FEATURE SCREENING

ANNALS OF STATISTICS, 41(4), 2123–2148.

By: J. Chang n, C. Tang n & Y. Wu n

author keywords: Empirical likelihood; high-dimensional data analysis; sure independence screening; large deviation
TL;DR: It is found that the marginal empirical likelihood ratio evaluated at zero can be used to differentiate whether an explanatory variable is contributing to a response variable or not, and a unified feature screening procedure for linear models and the generalized linear models is proposed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2013 journal article

Parametrically guided generalised additive models with application to mergers and acquisitions data

JOURNAL OF NONPARAMETRIC STATISTICS, 25(1), 109–128.

By: J. Fan*, A. Maity n, Y. Wang* & Y. Wu n

Contributors: J. Fan*, A. Maity n, Y. Wang* & Y. Wu n

author keywords: generalised additive model; leveraged buyout; local polynomial; mergers and acquisitions; parametric guide
TL;DR: An estimation procedure where the prior information is used as a parametric guide to fit the additive model is proposed and it is shown that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping theAsymptosis variance same as the unguided estimator. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2012 journal article

Elastic net for Cox's proportional hazards model with a solution path algorithm

Statistica Sinica, 22(1), 271–294.

By: Y. Wu

Source: NC State University Libraries
Added: August 6, 2018

2012 journal article

Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 107(497), 214–222.

By: L. Wang*, Y. Wu n & R. Li*

author keywords: Penalized quantile regression; SCAD; Sparsity; Ultra-high-dimensional data
TL;DR: A novel, sufficient optimality condition that relies on a convex differencing representation of the penalized loss function and the subdifferential calculus is introduced that enables the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2011 journal article

An ordinary differential equation-based solution path algorithm

JOURNAL OF NONPARAMETRIC STATISTICS, 23(1), 185–199.

By: Y. Wu n

author keywords: generalised linear model; LARS; LASSO; ordinary differential equation; solution path algorithm; QuasiLARS; quasi-likelihood model
TL;DR: This work proposes an extension of the LAR for generalised linear models and the quasi-likelihood model by showing that the corresponding solution path is piecewise given by solutions of ordinary differential equation (ODE) systems. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2011 journal article

Asymptotic properties of sufficient dimension reduction with a diverging number of predictors

Statistica Sinica, 21(2), 707–730.

By: Y. Wu & L. Li

Source: NC State University Libraries
Added: August 6, 2018

2011 journal article

Hard or Soft Classification? Large-Margin Unified Machines

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 106(493), 166–177.

By: Y. Liu*, H. Zhang n & Y. Wu n

author keywords: Class probability estimation; DWD; Fisher consistency; Regularization; SVM
TL;DR: A novel family of large- margin classifiers, namely large-margin unified machines (LUMs), which covers a broad range of margin-based classifiers including both hard and soft ones are proposed and theoretical consistency and numerical performance of LUMs are explored. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2011 journal article

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

JOURNAL OF NONPARAMETRIC STATISTICS, 23(2), 415–437.

By: Y. Liu* & Y. Wu n

author keywords: asymptotic normality; kernel; multiple quantile regression; non-crossing; oracle property; regularisation; variable selection
TL;DR: This paper proposes a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR), which uses kernel representations for QR functions and applies constraints on the kernel coefficients to avoid crossing. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2010 journal article

Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 19(1), 74–95.

By: Z. Zhu n & Y. Wu n

author keywords: Kriging; Local linear smoothing; Matern covariance function; Modified Bessel function; Tapering
TL;DR: This article model the spatial process as a convolution of independent Gaussian processes, with the spatially varying kernel function given by the modified Bessel functions, a generalization of the process-convolution approach of Higdon, Swall, and Kern (1999). (via Semantic Scholar)
UN Sustainable Development Goal Categories
2. Zero Hunger (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2010 journal article

Robust Model-Free Multiclass Probability Estimation

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 105(489), 424–436.

By: Y. Wu n, H. Zhang n & Y. Liu n

author keywords: Fisher consistency; Hard classification; Multicategory classification; Probability estimation; Soft classification; SVM
TL;DR: A model-free procedure to estimate multiclass probabilities based on large-margin classifiers by solving a series of weighted large- margin classifiers and then systematically extracting the probability information from these multiple classification rules is developed. (via Semantic Scholar)
UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2010 journal article

The MicroArray Quality Control (MAQC)-IIII study of common practices for the development and validation of microarray-based predictive models

Nature Biotechnology, 28(8), 827–109.

By: L. Shi, G. Campbell, W. Jones, F. Campagne, Z. Wen, S. Walker, Z. Su, T. Chu ...

Source: NC State University Libraries
Added: August 6, 2018

2010 conference paper

Utility-based weighted multicategory robust support vector machines

Statistics and its Interface, 3(4), 465–475.

By: Y. Liu*, Y. Wu n & Q. He*

TL;DR: It is shown that surprisingly, the cost- based weights do not work well for weighted extensions of the RSVM, and a novel utility-based weights for the weighted RSVM is proposed, as an extension of the standard SVM. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

2010 journal article

Varying-coefficient functional linear regression

BERNOULLI, 16(3), 730–758.

By: Y. Wu n, J. Fan* & H. Mueller

author keywords: asymptotics; eigenfunctions; functional data analysis; local polynomial smoothing; longitudinal data; varying-coefficient models
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Azithromycin treatment alters gene expression in inflammatory, lipid metabolism, and cell cycle pathways in well-differentiated human airway epithelia

PLoS One, 4(6).

By: C. Ribeiro, H. Hurd, Y. Wu, M. Martino, L. Jones, B. Brighton, R. Boucher, W. O'Neal

Source: NC State University Libraries
Added: August 6, 2018

2009 journal article

LOCAL QUASI-LIKELIHOOD WITH A PARAMETRIC GUIDE

ANNALS OF STATISTICS, 37(6B), 4153–4183.

By: J. Fan n, Y. Wu n & Y. Feng n

author keywords: Generalized linear model; local polynomial smoothing; parametric guide; quasi-likelihood method
TL;DR: This work proposes two parametrically guided nonparametric estimation schemes by incorporating prior shape information on the link transformation of the response variable's conditional mean in terms of the predictor variable. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Source: Web Of Science
Added: August 6, 2018

2009 journal article

NETWORK EXPLORATION VIA THE ADAPTIVE LASSO AND SCAD PENALTIES

ANNALS OF APPLIED STATISTICS, 3(2), 521–541.

By: J. Fan n, Y. Feng n & Y. Wu n

author keywords: Adaptive LASSO; covariance selection; Gaussian concentration graphical model; genetic network; LASSO; precision matrix; SCAD
TL;DR: Non-concave penalties and the adaptive LASSO penalty are introduced to attenuate the bias problem in the network estimation to solve the problem of precision matrix estimation. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2009 journal article

Stepwise multiple quantile regression estimation using non-crossing constraints

Statistics and Its Interface, 2(3), 299–310.

By: Y. Wu n & Y. Liu*

Source: NC State University Libraries
Added: August 6, 2018

2009 journal article

Ultrahigh dimensional feature selection: Beyond the linear model

Journal of Machine Learning Research, 10, 2013–2038.

By: J. Fan, R. Samworth & Y. Wu

Source: NC State University Libraries
Added: August 6, 2018

2009 journal article

Variable selection in quantile regression

Statistica Sinica, 19(2), 801–817.

By: Y. Wu & Y. Liu

Source: NC State University Libraries
Added: August 6, 2018

2008 journal article

Semiparametric Estimation of Covariance Matrixes for Longitudinal Data

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 103(484), 1520–1533.

By: J. Fan n & Y. Wu n

author keywords: Correlation structure; Difference-based estimation; Quasi-maximum likelihood; Varying-coefficient partially linear model
TL;DR: This work considers the possibility of rough mean regression function and introduces the difference-based method to reduce biases in the context of varying-coefficient partially linear mean regression models and provides a more robust estimator of the covariance function under a wider range of situations. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

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