Rohit Deo

About

The page provides information about the New York University Stern Center for Research Computing (SCRC), which is dedicated to providing world-class computational facilities and services to researchers at the Stern School of Business. The center offers a variety of services including a moderately sized Slurm HPC cluster, Cloud Computing (Virtual Machines), data acquisition and storage, research software, and access to WRDS (Wharton Research Data System). The research software suite is designed to facilitate advanced computational research and data analysis, while the datasets are sourced from diverse disciplines through collaborations with data repositories, platforms, and academic institutions. The compute services and storage systems support faculty and researchers' projects by providing high-speed, robust, and scalable solutions to meet diverse computational and storage needs.

Research topics

• Mathematics
• Statistics
• Econometrics
• Applied mathematics
• Computer science

Selected publications

• Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

  Journal of Econometrics · 2005-03-11 · 196 citations

  article1st authorCorresponding
  PublisherDOI

• Power Transformations to Induce Normality and their Applications

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

  articleOpen accessSenior author

  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

• Long memory in intertrade durations, counts and realized volatility of NYSE stocks

  Journal of Statistical Planning and Inference · 2010-06-02 · 50 citations

  article1st authorCorresponding
  PublisherDOI

• BIAS REDUCTION AND LIKELIHOOD-BASED ALMOST EXACTLY SIZED HYPOTHESIS TESTING IN PREDICTIVE REGRESSIONS USING THE RESTRICTED LIKELIHOOD

  Econometric Theory · 2009-09-03 · 38 citations

  articleSenior authorCorresponding

  Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter, and we propose basing inference on the restricted likelihood, which is free of such nuisance location parameters and also possesses small curvature, making it suitable for inference. The bias of the restricted maximum likelihood (REML) estimates is shown to be approximately 50% less…

  PublisherDOI

• Long Memory in Nonlinear Processes

  Lecture notes in statistics · 2006-01-01 · 15 citations

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

Frequent coauthors

• Willa W. Chen

  Texas A&M University

  32 shared

• Clifford M. Hurvich

  22 shared

• Philippe Soulier

  8 shared

• Meng‐Chen Hsieh

  7 shared

• Willa Chen

  Carolinas Medical Center

  4 shared

• Matthew Richardson

  3 shared

• Yi Lu

  2 shared

• Yi Wang

  University of South Carolina

  2 shared

Awards & honors

• Multa Scripsit Award
• NYU Distinguished Teaching Award
• Great Professor Award, Executive MBA programme
• Professor of the Year Award in the full-time MBA programme

Similar researchers at New York University

• Michael Feldmanh-73
• Anindya Ghoseh-60
• Alexander Tuzhilinh-49