Publications

 

* Rigas A.G., 1991, Spectra-based estimates of certain time-domain parameters of a bivariate stationary-point process.

         Math. Biosci., 104(2), 185-201.

         doi:10.1016/0025-5564(91)90061-M  [PDF (749 K)]

 

* Rigas A.G., 1992, Spectral analysis of stationary point processes using the fast Fourier transform algorithm.

         J. Time Ser. Anal., 13(5), 441-450. [PDF (4.16 M)]

 

* Rigas A.G., 1996, Spectral analysis of a stationary bivariate point process with application to neurophysiological

         problems. J. Time Ser. Anal., 17(2), 171-187. [PDF (5.86 M)]

 

* Rigas A.G., 1996, Estimation of certain parameters of a stationary hybrid process involving a time series and a

         point process. Math. Biosci., 133(2), 197-218.

         doi:10.1016/0025-5564(95)00106-9  [PDF (801 K)]

 

* Rigas A.G., 1996, Stochastic modeling of a complex physiological system. Proceedings of the International Conference

         on Differential Equations and their Application to Biology and Industry, World Scientific, Singapore, 409-415.

         [JPG (5.34 M)]                                                     

 

* Rigas A.G. and Tsitsis  D.S., 1996, Spectral analysis techniques of stationary point processes: extensions and applications

    to neurophysiological problems. Comput. Math. Applic., 32(11), 93-99.

   doi:10.1016/S0898-1221(96)00201-5  [PDF (355 K)]

 

* Rigas A.G. and Liatsis P., 2000, Identification of a neuroelectric system involving a single input and a single output.

                     Signal Process., 80(9), 1883-1894.

                     doi:10.1016/S0165-1684(00)00095-5  [PDF (250 K)]

 

* Vassiliadis V.G. and Rigas A.G., 2002, Phase recovery of a stochastic point process system. In Liatsis, P. (Ed.), Proc. 9th

                     International Workshop on Systems, Signals and Image Processing, World Scientific, Singapore, 37-43.

                      http://www.springerlink.com/content/x863672225204384/ [PDF (172 K)]

 

* Kotti V.K. and Rigas A.G., 2003, Identification of a complex neurophysiological system using the maximum likelihood

         approach. J. Biol. Systems, 11(2), 189-204.

         doi:10.1142/S0218339003000798

 

* Kotti V.K. and Rigas A.G., 2004, Improving the performance of a stochastic model by incorporating a non-linear term: application

          to a spiking biological system. Far East J. Theo. Stat., 14(1), 57-77.

                     http://www.pphmj.com/abstract/212.htm

 

* Kotti V.K. and Rigas A.G., 2005, Logistic Regression Methods and their Implementation. In  Edler L. and Kitsos C.P. (Eds.)

          Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment, Wiley, 355-369.

         [JPG (10.6 M)]                                                      

 

* Karavasilis G.J., Kotti V.K., Tsitsis D.S., Vassiliadis V.G. and Rigas A.G., 2005, Statistical methods and software for

                  risk assessment: applications to a neurophysiological data set. Comput. Stat. Data Anal., 49(1), 243-263.

                  doi:10.1016/j.csda.2004.05.010  [PDF (427 K)]

                                                           

* Vassiliadis V.G. and Rigas A.G., 2006, Consistent estimates of third-order spectral density function of a stationary point

                     process: application to neurophysiological problems. Far East J. Theo. Stat., 19(2), 245-271.

                     http://www.pphmj.com/abstract/2143.htm

 

* Rigas A.G.  and Vassiliadis V.G., 2007, A semi-parametric approach for comparing the estimated spectra of two stationary point

processes. Math. Biosci., 210(2), 361-377.

doi:10.1016/j.mbs.2007.02.012 

 

* Karavasilis G.J.,and Rigas A.G., 2007, Pointwise confidence intervals for certain modified parameters of stationary point processes. Applications to a complex neurophysiological system. JP J.  Biostat., 1(2), 155-166.

                     http://pphmj.com/abstract/2813.htm

 

* Kotti, V.K. Rigas, A.G., 2008, A Monte Carlo method used for the identification of the muscle spindle, in: Deutsch, A., Bravo de la Parra, R., Boer, R.J., Diekmann, O., Jagers, P., Kisdi, E., Kretzschmar, M., Lansky, P., Metz, H. (Eds.), Mathematical Modeling of Biological Systems, Volume II. Birkhauser, Boston, pp. 237-243.

         http://www.springerlink.com/content/x863672225204384/  [PDF (152 K)]

 

* Vassiliadis V.G. and Rigas A.G.  2009, A semi-parametric test based on a quasi-likelihood approach for comparing the estimated spectra of two stationary point processes. Neurocomputing, 72, 3212-3219.

   doi:10.1016/j.neucom.2009.02.008

 

* Vassiliadis V.G. and Rigas A.G. 2009, A new formulation of the Hinich's bispectral test for linearity based on a novel Q-Q plot

for testing distributional hypotheses, in:  Kitsos, C.P., Caroni, C. (Eds.), e-Proc. International Conference on Cancer Risk Assessment 3.

[PDF (323 K)]

 

* Karavasilis G.J.,and Rigas A.G., 2009, The use of nonparametric methods of stationary point processes in the study of complex interactions in Neuromuscular system. Biological Systems, 17(4), 577-595.

                     doi: 10.1142/S0218339009003095

 

* Halliday D.M., Rosenberg J.R., Rigas  A. and Conway B.A., 2009, A periodogram-based test for weak stationarity and consistency between sections in time series, Journal of Neuroscience Methods, 180,138–146.

                     doi:10.1016/j.jneumeth.2009.03.009

 

* A.G. Rigas and V.G. Vassiliadis, 2009, A non-parametric test based on cumulative periodograms for comparing two neural spike trains, Biometrie und Medizinische Informatik, 15, 103-111.

                         http://www.medizin.uni-greifswald.de/biometrie/publikationen.html   [PDF (7.03 M)]