World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
32
Citations
6606
World Ranking
3126
National Ranking
1253

Research.com Recognitions

  • 2010 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Jane-Ling Wang is affiliated with the University of California, Davis in the United States. Their research is primarily situated within the field of Mathematics, with a strong focus on Statistics and Probability. The scope of their work extends to multiple subfields, including Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, and Health, Toxicology and Mutagenesis.

Their research covers a range of topics, notably:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Functional Brain Connectivity Studies
  • Advanced Neuroimaging Techniques and Applications
  • Machine Learning and Data Classification
  • Health, Environment, Cognitive Aging
  • Metabolomics and Mass Spectrometry Studies

Jane-Ling Wang has contributed to several peer-reviewed publication venues. Frequent venues include:

  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Biometrika
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neural Networks

Their recent papers demonstrate a range of interdisciplinary research interests and cover various aspects of statistical methodology and application in artificial intelligence and neuroscience. Selected recent publications include:

  • "ML-LOO: Detecting Adversarial Examples with Feature Attribution," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Emotional EEG classification using connectivity features and convolutional neural networks," 2020, Neural Networks
  • "Mountaineers on Mount Everest: Effects of age, sex, experience, and crowding on rates of success and death," 2020, PLoS ONE
  • "Deep learning for the partially linear Cox model," 2022, The Annals of Statistics
  • "Mean and Covariance Estimation for Functional Snippets," 2020, OPAL (Open@LaTrobe) (La Trobe University)

Frequent collaborators in Jane-Ling Wang's research include:

  • Qixian Zhong
  • Yaqing Chen
  • Hans-Georg Müller
  • Muriel Bruchhage
  • Sean Deoni

Among the recognitions received, Jane-Ling Wang was named a Fellow of the American Association for the Advancement of Science (AAAS) in 2010.

Best Publications

  • Functional Data Analysis for Sparse Longitudinal Data

    Fang Yao;Hans-Georg Müller;Jane-Ling Wang

  • Functional Data Analysis

    Jane-Ling Wang;Jeng-Min Chiou;Hans-Georg Müller

  • Functional linear regression analysis for longitudinal data

    Fang Yao;Hans-Georg Müller;Jane-Ling Wang

  • Properties of principal component methods for functional and longitudinal data analysis

    Peter Hall;Hans-Georg Müller;Jane-Ling Wang

  • Hazard rate estimation under random censoring with varying kernels and bandwidths.

    Hans-Georg Muller;Jane-Ling Wang

  • Joint modeling of survival and longitudinal data : Likelihood approach revisited

    Fushing Hsieh;Yi Kuan Tseng;Jane Ling Wang

  • From sparse to dense functional data and beyond

    Xiaoke Zhang;Jane-Ling Wang

  • Estimation for a partial-linear single-index model

    Jane Ling Wang;Liugen Xue;Lixing Zhu;Yun Sam Chong

  • Joint modelling of accelerated failure time and longitudinal data

    Yi-Kuan Tseng;Fushing Hsieh;Jane-Ling Wang

  • Density and hazard rate estimation for censored data via strong representation of the Kaplan-Meier estimator

    S. H. Lo;Y. P. Mack;J. L. Wang

  • FUNCTIONAL RESPONSE MODELS

    Jeng-Min Chiou;Hans-Georg Muller;Jane-Ling Wang

  • Strong Representations of the Survival Function Estimator for Truncated and Censored Data with Applications

    I. Gijbels;J.L. Wang

  • Functional canonical analysis for square integrable stochastic processes

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang

  • Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.

    Jimin Ding;Jane-Ling Wang

  • Review of Functional Data Analysis

    Jane-Ling Wang;Jeng-Min Chiou;Hans-Georg Mueller

  • Functional quasi-likelihood regression models with smooth random effects

    Jeng-Min Chiou;Hans-Georg Müller;Jane-Ling Wang

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Lina Boente Boente;David E. Tyler;Jane Ling Wang

  • Dimension reduction for censored regression data

    Ker-Chau Li;Jane-Ling Wang;Chun-Houh Chen

  • COVARIATE ADJUSTED FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS FOR LONGITUDINAL DATA

    Ci-Ren Jiang;Jane-Ling Wang

  • Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

    Puyudi Yang;Jianbo Chen;Cho-Jui Hsieh;Jane-Ling Wang

  • Locally adaptive hazard smoothing

    Hans-Georg Müller;Jane-Ling Wang

  • Analysis of oldest-old mortality: lifetables revisited

    Jane-Ling Wang;Hans-Georg Müller;William B. Capra

  • A comparison of hazard rate estimators for left truncated and right censored data

    Unknown

  • Methods of canonical analysis for functional data

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang

  • A FUNCTIONAL MULTIPLICATIVE EFFECTS MODEL FOR LONGITUDINAL DATA, WITH APPLICATION TO REPRODUCTIVE HISTORIES OF FEMALE MEDFLIES.

    Jeng-Min Chiou;Hans-Georg Müller;Jane-Ling Wang;James R. Carey

  • Spontaneous neural fluctuations predict decisions to attend

    Jesse J. Bengson;Todd A. Kelley;Xiaoke Zhang;Jane-Ling Wang

  • Two-Sample Inference for Median Survival Times Based on One-Sample Procedures for Censored Survival Data

    Jane-Ling Wang;Thomas P. Hettmansperger

  • Robust functional principal components: A projection-pursuit approach

    Juan Lucas Bali;Graciela Boente;David E. Tyler;Jane-Ling Wang

  • Functional linear regression via canonical analysis

    Guozhong He;Hans-Georg Müller;Jane-Ling Wang;Wenjing Yang

  • Nonparametric Regression Analysis of Longitudinal Data

    J. ‐L. Wang

Frequent Co-Authors

Hans-Georg Müller
Hans-Georg Müller University of California, Davis
Linda Partridge
Linda Partridge Max Planck Society
Winfried Stute
Winfried Stute University of Giessen
Richard M. Martin
Richard M. Martin University of Bristol
James W. Vaupel
James W. Vaupel University of Southern Denmark
Lixing Zhu
Lixing Zhu Beijing Normal University
Sean C.L. Deoni
Sean C.L. Deoni Brown University
Michael S. Kramer
Michael S. Kramer McGill University
Thomas P. Hettmansperger
Thomas P. Hettmansperger Pennsylvania State University
Joseph L. Gastwirth
Joseph L. Gastwirth George Washington University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students studying Mathematics in the USA, exploring related online degrees can open diverse career opportunities. For instance, pursuing marketing graduate programs can complement mathematical skills with insights in consumer behavior and analytics, enhancing roles in data-driven marketing strategies.

Many professionals also consider business leadership paths through accelerated programs like the best 1 year mba programs. These allow focused study in areas such as finance and operations, which align well with quantitative backgrounds.

Additionally, options that accept transfer credits provide flexibility. Exploring mba transfer credits can reduce time and cost while advancing career prospects in management and strategic roles.

Lastly, mastering data-driven insights through one of the best masters in data analytics programs can be particularly rewarding. These programs harness mathematical expertise for careers in big data, business intelligence, and AI, sectors experiencing rapid growth.

Best Scientists Citing Jane-Ling Wang

Trending Scientists

Recently Published Articles