Marco Minozzo

Personal Details

First Name:	Marco
Middle Name:
Last Name:	Minozzo
Suffix:
RePEc Short-ID:	pmi539
	http://www.dse.univr.it/?ent=persona&id=3891&lang=en

Affiliation

Dipartimento di Scienze Economiche
Facoltà di Economia
Università degli Studi di Verona

Verona, Italy
http://www.dse.univr.it/
RePEc:edi:isverit (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Marco Minozzo & Silvia Centanni, 2012. "Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes," Working Papers 11/2012, University of Verona, Department of Economics.

   Cited by:

   1. Alan Genaro & Adilson Simonis, 2015. "Estimating doubly stochastic Poisson process with affine intensities by Kalman filter," Statistical Papers, Springer, vol. 56(3), pages 723-748, August.

2. Marco Minozzo & Clarissa Ferrari, 2011. "Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy)," Working Papers 21/2011, University of Verona, Department of Economics.

   Cited by:

   1. Marco Minozzo & Luca Bagnato, 2021. "A unified skew‐normal geostatistical factor model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.

3. Marco Minozzo, 2011. "On the existence of some skew normal stationary processes," Working Papers 20/2011, University of Verona, Department of Economics.

   Cited by:

   1. Marco Minozzo & Luca Bagnato, 2021. "A unified skew‐normal geostatistical factor model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
   2. M. Alodat & M. AL-Rawwash, 2014. "The extended skew Gaussian process for regression," METRON, Springer;Sapienza Università di Roma, vol. 72(3), pages 317-330, October.
   3. Stéphane Lhuissier, 2019. "Bayesian Inference for Markov-switching Skewed Autoregressive Models," Working papers 726, Banque de France.
   4. Chunsheng Ma, 2013. "Mittag-Leffler vector random fields with Mittag-Leffler direct and cross covariance functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 941-958, October.
   5. Marco Minozzo & Clarissa Ferrari, 2012. "Monte Carlo likelihood inference in multivariate model-based geostatistics," Working Papers 33/2012, University of Verona, Department of Economics.
   6. Jiangyan Wang & Miao Yang & Anandamayee Majumdar, 2018. "Comparative study and sensitivity analysis of skewed spatial processes," Computational Statistics, Springer, vol. 33(1), pages 75-98, March.

Articles

1. Marco Minozzo & Clarissa Ferrari, 2013. "Multivariate geostatistical mapping of radioactive contamination in the Maddalena Archipelago (Sardinia, Italy): spatial special issue," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 195-213, April.

   Cited by:

   1. Alessandro Fassò & Alessio Pollice & Barbara Cafarelli, 2013. "Spatial statistics for environmental studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 89-91, April.
   2. Marco Minozzo & Clarissa Ferrari, 2012. "Monte Carlo likelihood inference in multivariate model-based geostatistics," Working Papers 33/2012, University of Verona, Department of Economics.

2. Centanni, Silvia & Minozzo, Marco, 2006. "A Monte Carlo Approach to Filtering for a Class of Marked Doubly Stochastic Poisson Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1582-1597, December.

   Cited by:

   1. Fernández-Alcalá, R.M. & Navarro-Moreno, J. & Ruiz-Molina, J.C., 2009. "Statistical inference for doubly stochastic multichannel Poisson processes: A PCA approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4322-4331, October.
   2. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Yang, Xinda, 2021. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 9-24.
   3. Marco Minozzo & Silvia Centanni, 2012. "Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes," Working Papers 11/2012, University of Verona, Department of Economics.
   4. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
   5. James Martin & Ajay Jasra & Emma McCoy, 2013. "Inference for a class of partially observed point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 413-437, June.
   6. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
   7. Benjamin Avanzi & Gregory Clive Taylor & Bernard Wong & Xinda Yang, 2020. "On the modelling of multivariate counts with Cox processes and dependent shot noise intensities," Papers 2004.11169, arXiv.org, revised Dec 2020.
   8. D. T. Koops & O. J. Boxma & M. R. H. Mandjes, 2017. "Networks of $$\cdot /G/\infty $$ · / G / ∞ queues with shot-noise-driven arrival intensities," Queueing Systems: Theory and Applications, Springer, vol. 86(3), pages 301-325, August.
   9. Pierre Del Moral & Ajay Jasra & Yan Zhou, 2017. "Biased Online Parameter Inference for State-Space Models," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 727-749, September.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-MST: Market Microstructure (1) 2011-01-03
2. NEP-ORE: Operations Research (1) 2011-01-03

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