Alexandra M. Schmidt
Alexandra M. Schmidt
Verifisert e-postadresse på - Startside
Sitert av
Sitert av
Nonstationary multivariate process modeling through spatially varying coregionalization
AE Gelfand, AM Schmidt, S Banerjee, CF Sirmans
Test 13 (2), 263-312, 2004
Bayesian inference for non‐stationary spatial covariance structure via spatial deformations
AM Schmidt, A O'Hagan
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2003
A Bayesian coregionalization approach for multivariate pollutant data
AM Schmidt, AE Gelfand
Journal of Geophysical Research 108 (D24), 8783, 2003
Modelling species diversity through species level hierarchical modelling
AE Gelfand, AM Schmidt, S Wu, JA Silander Jr, A Latimer, AG Rebelo
Journal of the Royal Statistical Society: Series C (Applied Statistics) 54 …, 2005
Spatio‐temporal models for mapping the incidence of malaria in Pará
AA Nobre, AM Schmidt, HF Lopes
Environmetrics: The official journal of the International Environmetrics …, 2005
Considering covariates in the covariance structure of spatial processes
AM Schmidt, P Guttorp, A O'Hagan
Environmetrics 22, 487-500, 2011
A class of covariate-dependent spatiotemporal covariance functions
BJ Reich, J Eidsvik, M Guindani, AJ Nail, AM Schmidt
The annals of applied statistics 5 (4), 2265, 2011
Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency
AM Schmidt, ARB Moreira, SM Helfand, TCO Fonseca
Journal of Productivity Analysis 31 (2), 101-112, 2009
Modelling zero-inflated spatio-temporal processes
MVM Fernandes, AM Schmidt, HS Migon
Statistical Modelling 9 (1), 3-25, 2009
Stochastic search algorithms for optimal design of monitoring networks
R Ruiz‐Cárdenas, MAR Ferreira, AM Schmidt
Environmetrics: The official journal of the International Environmetrics …, 2010
Accounting for spatially varying directional effects in spatial covariance structures
JHV Neto, AM Schmidt, P Guttorp
Journal of the Royal Statistical Society Series C (Applied Statistics) 63 (1 …, 2014
Bayesian spatio-temporal models based on discrete convolutions.
B Sansó, AM Schmidt, AA Nobre
The Canadian Journal of Statistics 36 (2), 239-258, 2008
A joint model for rainfall–runoff: the case of Rio Grande Basin
RR Ravines, AM Schmidt, HS Migon, CD Rennó
Journal of Hydrology 353 (1-2), 189-200, 2008
Revisiting distributed lag models through a Bayesian perspective
R R. Ravines, A M. Schmidt, H S. Migon
Applied Stochastic Models in Business and Industry 22 (2), 193-210, 2006
Modelling multivariate counts varying continuously in space (with discussion)
AM Schmidt, MA Rodrıguez
Bayesian Statistics 9, 611-638, 2011
Multivariate spatial process models: conditional and unconditional Bayesian approaches using coregionalization
AE Gelfand, AM Schmidt, CF Sirmans
Center for Real Estate and Urban Economic Studies, University of Connecticut, 2002
Hyperparameter estimation in forecast models
HF Lopes, ARB Moreira, AM Schmidt
Computational statistics & data analysis 29 (4), 387-410, 1999
Spatial modelling of the relative risk of dengue fever in Rio de Janeiro for the epidemic period between 2001 and 2002
GS Ferreira, AM Schmidt
Brazilian journal of Probability and Statistics, 29-47, 2006
An efficient sampling scheme for dynamic generalized models
HS Migon, AM Schmidt, R Ravines, JBM Pereira
Computational Statistics, DOI 10.1007/s00180-013-0406-9, 2013
Modelling Time Series of Counts in Epidemiology
AM Schmidt, JBM Pereira
International Statistical Review 79, 48-69, 2011
Systemet kan ikke utføre handlingen. Prøv igjen senere.
Artikler 1–20