Dimitris Politis

About

Dimitris Politis is a professor in the Department of Mathematics at the University of California, San Diego. He holds a Ph.D. in Statistics from Stanford University, obtained in 1990. His research areas include statistics, nonparametrics, bootstrap methods, and time series analysis. He has been recognized with honors such as a Guggenheim Fellowship and is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. His work focuses on advanced statistical methodologies and their applications, contributing significantly to the fields of statistical theory and practice.

Research topics

• Artificial Intelligence
• Statistics
• Mathematics
• Computer Science
• Algorithm
• Econometrics
• Geology

Selected publications

• Ridge regression revisited: Debiasing, thresholding and bootstrap

  The Annals of Statistics · 2022 · 16 citations

  Senior authorCorresponding

  The success of the Lasso in the era of high-dimensional data can be attributed to its conducting an implicit model selection, that is, zeroing out regression coefficients that are not significant. By contrast, classical ridge regression cannot reveal a potential sparsity of parameters, and may also introduce a large bias under the high-dimensional setting. Nevertheless, recent work on the Lasso involves debiasing and thresholding, the latter in order to further enhance the model selection. As a…

  DOI

• Asymptotic validity of bootstrap confidence intervals in nonparametric regression without an additive model

  Electronic Journal of Statistics · 2021 · 14 citations

  Senior authorCorresponding

  Bootstrap for nonparametric regression has been around for more than 30 years. Nevertheless, most results are based on assuming an additive regression model with respect to independent and identical (i.i.d.) errors. An exception is the Local Bootstrap of Shi [23] for which, however, no bootstrap consistency results are available. We attempt to remedy this here while at the same time showing bootstrap consistency for a more general class of methods that fall under the heading of Model-free Bootst…

  DOI

• Prepivoted Augmented Dickey-Fuller Test with Bootstrap-Assisted Lag Length Selection

  Stats · 2024-10-17 · 7 citations

  articleOpen accessSenior authorCorresponding

  We investigate the application of prepivoting in conjunction with lag length selection to correct the size and power performance of the Augmented Dickey-Fuller test for a unit root. The bootstrap methodology used to perform the prepivoting is a residual based AR bootstrap that ensures that bootstrap replicate time series are created under the null irrespective of whether the originally observed series obeys the null hypothesis or not. Simulation studies wherein we examine the performance of our…

  PublisherOA PDFDOI

• Debiased and thresholded ridge regression for linear models with heteroskedastic and correlated errors

  Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2023-03-06 · 6 citations

  articleOpen accessSenior authorCorresponding

  Abstract High-dimensional linear models with independent errors have been well-studied. However, statistical inference on a high-dimensional linear model with heteroskedastic, dependent (and possibly nonstationary) errors is still a novel topic. Under such complex assumptions, the paper at hand introduces a debiased and thresholded ridge regression estimator that is consistent, and is able to recover the model sparsity. Moreover, we derive a Gaussian approximation theorem for the estimator, and…

  PublisherOA PDFDOI

• Challenges and Opportunities for Statistics in the Era of Data Science

  Harvard Data Science Review · 2025-04-21 · 3 citations

  articleOpen access

  Statistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. While at the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions, nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory and computational aspects and often driven by other disciplines, such as computer…

  PublisherOA PDFDOI

Recent grants

• Computer-intensive methods for nonparametric time series analysis

  NSF · $140k · 2007–2010

• Computer-intensive methods for nonparametric time series analysis

  NSF · $240k · 2013–2017

• Computer-intensive methods for nonparametric time series analysis'

  NSF · $275k · 2010–2014

Frequent coauthors

• Tucker McElroy

  United States Census Bureau

  51 shared

• Joseph P. Romano

  Stanford University

  48 shared

• Keh‐Shin Lii

  University of California, Riverside

  40 shared

• Efstathios Paparoditis

  University of Cyprus

  37 shared

• Richard A. Davis

  19 shared

• Richard A. Davis

  Columbia University

  19 shared

• Michael Wolf

  19 shared

• Halbert White

  University of California, San Diego

  16 shared

Awards & honors

• Guggenheim Fellowship
• Fellow of the American Statistical Association
• Fellow of the Institute of Mathematical Statistics

Similar researchers at University of California, San Diego

• Natalia Komarovah-62
• David Meyerh-45
• Michael Holsth-40