Wei "Wayne" Chen

About

Wei "Wayne" Chen is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. He holds a Ph.D. in Mechanical Engineering from the University of Maryland, College Park, obtained in 2019, and both his M.S. and B.S. degrees in Mechanical Engineering from Chongqing University in China, earned in 2015 and 2012 respectively. His research interests include generative design, artificial intelligence and machine learning, uncertainty quantification, and advanced manufacturing. Chen has received several awards and honors, such as the ASME Journal of Mechanical Design Reviewer of the Year Award in 2023, the ASME DAC Best Paper Award in 2022, and an Editors’ Choice Honorable Mention from the Journal of Mechanical Design in 2021. His scholarly work involves developing innovative computational methods and models to support engineering design, with a focus on leveraging AI and data-driven approaches to improve design processes and material functionalities.

Research topics

• Mathematics
• Statistics
• Applied mathematics
• Econometrics
• Algorithm

Selected publications

• Power Transformations to Induce Normality and their Applications

  Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2003-12-22 · 54 citations

  articleOpen access1st authorCorresponding

  Summary Random variables which are positive linear combinations of positive independent random variables can have heavily right-skewed finite sample distributions even though they might be asymptotically normally distributed. We provide a simple method of determining an appropriate power transformation to improve the normal approximation in small samples. Our method contains the Wilson–Hilferty cube root transformation for χ2 random variables as a special case. We also provide some important exa…

  PublisherOA PDFDOI

• A GENERALIZED PORTMANTEAU GOODNESS-OF-FIT TEST FOR TIME SERIES MODELS

  Econometric Theory · 2004-02-10 · 19 citations

  articleOpen access1st authorCorresponding

  We present a goodness-of-fit test for time series models based on the discrete spectral average estimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statistic allows the null hypothesis to be either a short- or long-range dependence model. Our test is in the frequency domain, is easy to compute, and does not require the calculation of residuals from the fitted model. This is especially advantageous when the fitted model is not a finite-order autoregressive…

  PublisherOA PDFDOI

• Estimation of mis-specified long memory models

  Journal of Econometrics · 2005-08-11 · 11 citations

  article1st authorCorresponding
  PublisherDOI

• Semiparametric estimation of fractional cointegrating subspaces

  The Annals of Statistics · 2006-12-01 · 10 citations

  articleOpen access1st authorCorresponding

  We consider a common-components model for multivariate fractional cointegration, in which the s≥1 components have different memory parameters. The cointegrating rank may exceed 1. We decompose the true cointegrating vectors into orthogonal fractional cointegrating subspaces such that vectors from distinct subspaces yield cointegrating errors with distinct memory parameters. We estimate each cointegrating subspace separately, using appropriate sets of eigenvectors of an averaged periodogram matri…

  PublisherOA PDFDOI

• The restricted likelihood ratio test for autoregressive processes

  Journal of Time Series Analysis · 2011-11-29 · 9 citations

  article1st authorCorresponding

  The restricted likelihood is known to produce estimates with significantly less bias in AR( p ) models with intercept and/or trend. In AR(1) models, the corresponding restricted likelihood ratio test (RLRT), unlike the t ‐statistic or the usual LRT, has been recently shown to be well approximated by the chi‐square distribution even close to the unit root, thus yielding confidence intervals with good coverage properties. In this article, we extend this result to AR( p ) processes of arbitrary ord…

  PublisherDOI

Recent grants

• Long Memory Time Series Modelling: Computational and Statistical Efficiency, Nonstationarity/Noninvertibility and Goodness of Fit

  NSF · $116k · 2006–2010

• REML in time series models: Applications to unified inference in moderate and near integrated autoregressions, dynamic panels, cointegrated systems and non-linear IV regressions

  NSF · $143k · 2010–2014

• Fractional Cointegration, Tapering and Estimation of Misspecified Models in Long Memory Time Series

  NSF · $107k · 2003–2007

Frequent coauthors

• Rohit Deo

  New York University

  32 shared

• Clifford M. Hurvich

  16 shared

• Yi Lü

  Eye & ENT Hospital of Fudan University

  2 shared

• Yanping Yi

  Zhejiang University of Finance and Economics

  2 shared

• Califford M. Hurvich

  1 shared

Awards & honors

• Reviewer of the Year Award - 2023
• ASME DAC Best Paper Award - 2022
• Journal of Mechanical Design Editors’ Choice Honorable Menti…

Similar researchers at Texas A&M University

• Guillermo Aguilarh-42
• Iman Borazjanih-39
• Mark Balash-39