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Mauricio Nascimento

Assistant Teaching Professor
Mauricio Nascimento


Mauricio Nascimento is an Assistant Teaching Professor of Statistics at Penn State.

Nascimento received his Ph.D. in Statistics from The Pennsylvania State University in 2020, and a bachelor in Statistics from the Federal University of Parana in 2012.

Nascimento studied the spatial relationship between multivariate extremes using a combination of semi-parametric spectral density and Gaussian process. He also proposed a faster approximation for high-dimensional Gaussian cumulative distribution.

Nascimento was an invited speaker at STATMOS Workshop on Climate Extremes in 2016. He joined Penn State as a Asst. Teaching Professor in 2020.


Honors and Awards

2018 Award for Support of Pedagogy in Undergraduate Instruction



Yung-Chen Jen Chiu, Hsiao-Ying Vicki Chang, Ann Johnston, Mauricio Nascimento, James T. Herbert and Xiaoyue Maggie Niu (2019). Impact of Disability Services on Academic Achievement among College Students with Disabilities. Journal of Postsecondary Education and Disability 32 (3).

Mauricio Nascimento, & Benjamin A. Shaby (2019). Spatial Semi-parametric Spectral Density Estimation for Multivariate Extremes, with Application to Fire Threat Journal of Environmental Statistics, 9(3).

Mauricio Nascimento, & Benjamin A. Shaby (2021). A Vecchia approximation for high-dimensional Gaussian cumulative distribution functions arising from spatial data Journal of Statistical Computation and Simulation.


  • STAT 100 - Statistical Concepts and Reasoning
  • STAT 200 - Elementary Statistics
  • STAT 319 - Elementary Mathematical Statistics
  • STAT 380 - Data Science Through Statistical Reasoning and Computation
  • STAT 401 - Experimental Methods
  • STAT 414 - Introduction to Probability Theory
  • STAT 418 - Introduction to Probability and Stochastic Processes of Engineering
  • STAT 462 - Applied Regression Analysis
  • STAT 463 - Applied Time Series Analysis
  • STAT 466 - Survey Sampling
  • STAT 497 - Applied Bayesian Analysis
  • STAT 502 - Analysis of Variance and Design of Experiments
  • STAT 503 - Design of Experiments
  • STAT 508 - Applied Data Mining and Statistical Learning