| dc.creator | Dias R. | |
| dc.creator | Ferreira C.S. | |
| dc.creator | Garcia N.L. | |
| dc.date | 2008 | |
| dc.date | 2015-06-30T19:36:47Z | |
| dc.date | 2015-11-26T14:45:52Z | |
| dc.date | 2015-06-30T19:36:47Z | |
| dc.date | 2015-11-26T14:45:52Z | |
| dc.date.accessioned | 2018-03-28T21:55:14Z | |
| dc.date.available | 2018-03-28T21:55:14Z | |
| dc.identifier | | |
| dc.identifier | Statistical Inference For Stochastic Processes. , v. 11, n. 1, p. 11 - 34, 2008. | |
| dc.identifier | 13870874 | |
| dc.identifier | 10.1007/s11203-006-9005-5 | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-35648938116&partnerID=40&md5=cbf7933d86662c6285317ec6f697f5a6 | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/106839 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/106839 | |
| dc.identifier | 2-s2.0-35648938116 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1252614 | |
| dc.description | Let f: [a,b] → ℝ be an unknown 2 times differentiable function and consider M to be an α- homogeneous Poisson process on Graf(f). The goal is to estimate f having a sample of the inhomogeneous Poisson process N constructed by dislocating each point of M perpendicularly to Graf(f) by a normal random variable with zero mean and constant variance σ2. The exact formulas for the mean measure and the intensity function of N are obtained. Then, the function f is estimated directly using a hybrid spline approach to penalized maximum likelihood. Simulation results indicate the procedure to be consistent as α → ∞ and σ2 → 0. © 2006 Springer Science+Business Media, LLC. | |
| dc.description | 11 | |
| dc.description | 1 | |
| dc.description | 11 | |
| dc.description | 34 | |
| dc.description | Daley, D.J., Vere-Jones, D., (1988) An Introduction to the Theory of Point Processes, , Springer-Verlag New York | |
| dc.description | Dias, R., Density estimation via hybrid splines (1998) J Stat Comput Simul, 60, pp. 277-294 | |
| dc.description | Dias, R., Sequential adaptive non parametric regression via H-splines (1999) Commun Stat: Comput Simul, 28, pp. 501-515 | |
| dc.description | Dudewicz, E.J., Mishra, S.N., (1988) Modern Mathematical Statistics. Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics, , Wiley Inc. New York | |
| dc.description | Garcia, N.L., Maximum likelihood estimation and suboptimal stopping time for a Poisson point process (1995) Rebrape, 9, pp. 67-82. , 1 | |
| dc.description | Kutoyants, Y.A., Intensity parameter estimation of an inhomogeneous Poisson process (1979) Probl Control Inform Theory/Probl Upravlen Teor Inform, 8, pp. 137-149. , 2 | |
| dc.description | Kutoyants, Y.A., (1998) Statistical Inference for Spatial Poisson Processes, , Springer-Verlag New York Lecture Notes in Statistics, vol. 134 | |
| dc.description | Luo, Z., Wahba, G., Hybrid adaptive splines (1997) J Am Stat Assoc, 92, pp. 107-116 | |
| dc.description | Ramlau-Hansen, H., Smoothing counting process intensities by means of kernel functions (1983) Ann Stat, 11, pp. 453-466. , 2 | |
| dc.description | Resnick, S., (1992) Adventures in Stochastic Processes, , Birkhäuser Boston Inc. Boston, MA | |
| dc.description | Wahba, G., (1990) Spline Models for Observational Data, , SIAM Philadelphia, PA | |
| dc.description | Wegman, E.J., Wright, I.W., Splines in statistics (1983) J Am Stat Assoc, 78, pp. 351-365 | |
| dc.language | en | |
| dc.publisher | | |
| dc.relation | Statistical Inference for Stochastic Processes | |
| dc.rights | fechado | |
| dc.source | Scopus | |
| dc.title | Penalized Maximum Likelihood Estimation For A Function Of The Intensity Of A Poisson Point Process | |
| dc.type | Artículos de revistas | |
