University of Puerto Rico    (787) 764 0000

    Professor Information

    Luis Raul Pericchi

    Address Department of Mathematics, 17 University Ave. Ste 1701
    San Juan PR, 00925-2537
    Office NCN II C-121/ NCN A-206
    Hours
    Actual Program
    Telephone (787) 764-0000 88282/ 88283
    E-Mail moc.liamg@rpraul, ude.rpu@ihccirep.siul

    Education

    PhD, University of London, Imperial College,, 1981

    Research Interests

    Estadística Matemática, Aplicaciones de la Estadí­stica Bayesiana, Estadí­stica Computacional

    Selected Publications

    1. Velez D., Perez M.E. and Pericchi L.R. (2022) Increasing the Replicability for Linear Models via Adaptive Significance Levels”. TEST, Feb. 2022. https : //doi.org/10.1007/s11749 − 022 − 00803 − 4.
    2. Berger JO, Garcia-Donato G, Moreno E, and Pericchi LR (2022) ”Objective Bayesian Testing and Model Uncertainty”. Handbook of Bayesian, Fiducial, and Frequentist Inference. CRC Press.
    3. Correa-Alvarez, C.D., Salazar-Uribe, J.C. and Pericchi-Guerra, L.R. Bayesian multilevel logistic regression models: a case study applied to the results of two questionnaires administered to university students. Comput Stat (2022). https : //doi.org/10.1007/s00180−022 − 01287 − 4
    4. Soto-Salgado M, Suárez E, Viera-Rojas T, Pericchi L, Ramos-Cartagena J, Deshmukh A,Tirado-Gómez M and Ana Patricia Ortiz A. (2022) ”Development of a multivariable prediction model for anal high-grade squamous intraepithelial lesions in persons living with HIV in Puerto Rico: A cross-sectional study”. The Lancet RegionalHealth-Americas, 2022, 100382. https : //doi.org/10.1016/j.lana.2022.100382
    5. Li, A.; Pericchi, L.; Wang, K. Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations. Entropy 2020, 22, 513.
    6. Arturo J. Fernandez, Cristian D. Correa-Alvarez, Luis R. Pericchi. "Balancing producer and consumer risks in optimal attribute testing: A unified Bayesian/Frequentist design". European Journal of Operational Research.$DOI: https://doi.org/10.1016/j.ejor.2020.03.001$.
    7. Shiru Lin, Yekun Wang, Yinghe Zhao, Luis R. Pericchi, Arturo J. Hernández-Maldonado and Zhongfang Chen. (2020) "Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives", Journal of Materials Chemistry A, J. Mater. Chem. A, 2020, Advance Article, $https://doi.org/10.1039/C9TA11909D$.
    8. D. Fouskakis, J. K. Innocent and L. Pericchi (2020) Power-expected-posterior prior Bayes factor consistency for nested linear models with increasing dimensions, Statistical Theory and Related Fields, $DOI: 10.1080/24754269.2020.1719355$
    9. Pericchi L. (2020) Discussion on the meeting on‘Signs and sizes:understanding and replicating statistical findings’ J. R. Statist. Soc. A 183, Part 2, pp. 449–469
    10. Williams D.R. , Rast P., L.R. Pericchi and Mulder J. (2019) “Comparing Gaussian Graphical Models with the Posterior Predictive Distribution and Bayesian Model Selection”, Psychological Methods
    11. Bayarri M.J., Berger J.O., Jangc W., Rayd S., Pericchi L.R. and Visser I. (2019) Prior-based Bayesian Information Criterion (PBIC). {f Statistical Theory and Related Fields} (with discussion).Vol. 3, 1, p. 2-13 .$www.tandfonline.com/doi/full/10.1080/24754269.2019.1582126$
    12. Pericchi L.R. and Torres D. (2012) ”Quick anomaly detection by the Newcomb-Benford Law, with applications to electoral processes data from the USA, Puerto Rico and Venezuela” Statistical Science, 26,4, p. 502-516.
    13. Fuquene J.A., Cook J.D. and Pericchi L.R. (2009) "A Case for Robust Bayesian Priors with Applications to Clinical Trials", BAYESIAN ANALYSIS, Vol. 4, Number 4, pp. 817-846. Available electronically in: http://ba.stat.cmu.edu/vol04is04.
    14. Berger J.O. and Pericchi L.R.,  The Intrinsic Bayes Factor for Model Selection and Prediction.  Journal of the American Statistical Association, 91, 433 (1996), 109-122.
    15. Mendez B. and Pericchi L.R. (2009) Assessing Conditional Extremal Risk of Flooding in Puerto Rico. Stochastic Environmental Research and Risk Assessesment, 23, 3, pp.399-410.
    16. Berger J.O. and Pericchi L.R.  Training samples in objective Bayesian model selection.  The Annals of Statistics, Vol. 32, No. 3 (2004), 841-869.
    17. Pericchi, L.R.,   Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors.  Handbook of Statistics, D. Dey and C.R. Rao editors, Vol. 25 (2005), 115-149.

    Grants and Awards

    1. Elected Ordinary Member of the International Statistical Institute, 1989
    2. Guggenheim Fellowship (Jul.1997-Jun.1998)
    3. Principal Invesigator, 2006-2008 NSF Grant: "A Synthesis of Objective Bayes Factors for Model Selection and Hypothesis Testing". Amount: $75,000.
    4. Principal Invesigator, 2006-2011 NSF Grant: "Accelerating Puerto Rican students into the National Research Effort in Mathematics and Computer Science". Amount: $500,000.

    Additional Information

    Professor Pericchi has been elected member of the International Society for Bayesian Analysis (2013) and the American Statistical Association (2016). He is the current President of the American Statistical Association-Puerto Rico Chapter.
    Centro de Bioestadistica y Bioinformatica:

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