Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jan Vogler"'
Publikováno v:
Tissue Eng Regen Med
BACKGROUND: Osteochondral injury is a very common orthopaedic pathology, mainly affecting young, active population, with limited current treatment options. Herein we are presenting cellular and early clinical data of a patient series treated for chro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::335bab096895eb8f562733420070de36
https://doi.org/10.1007/s13770-020-00253-9
https://doi.org/10.1007/s13770-020-00253-9
Autor:
Jan Vogler, Vasyl Golosnoy
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d19848113a6f4d18341794982bb44b3
https://doi.org/10.2139/ssrn.4077801
https://doi.org/10.2139/ssrn.4077801
Autor:
Daniel Gingerich, Jan Vogler
Do pandemics have lasting consequences for political behavior? We examine the consequences of the most deadly pandemic in recorded history: the Black Death (1347-1351). Our claim is that pandemics can influence politics in the long run if they impose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdfdfa4c3ea3db8ccd55fbe9cec26765
https://doi.org/10.33774/apsa-2020-2c2fm-v6
https://doi.org/10.33774/apsa-2020-2c2fm-v6
Publikováno v:
Journal of Applied Econometrics. 32:600-620
Summary We develop a panel count model with a latent spatio-temporal heterogeneous state process for monthly severe crimes at the census-tract level in Pittsburgh, Pennsylvania. Our dataset combines Uniform Crime Reporting data with socio-economic da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85f7611b9914c05821828875453a58a1
https://doi.org/10.1002/jae.2534
https://doi.org/10.1002/jae.2534
We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c179c51847841e2e89f37aee4ea71fd
https://doi.org/10.1108/s0731-905320160000037009
https://doi.org/10.1108/s0731-905320160000037009
Autor:
Pavlo Mozharovskyi, Jan Vogler
Publikováno v:
Economics Letters
Economics Letters, Elsevier, 2016, 148, pp.87-90. ⟨10.1016/j.econlet.2016.09.022⟩
Economics Letters, Elsevier, 2016, 148, pp.87-90. ⟨10.1016/j.econlet.2016.09.022⟩
Composite Marginal Likelihood (CML) has become a popular approach for estimating spatial probit models. However, for spatial autoregressive specifications the existing brute-force implementations are infeasible in large samples as they rely on invert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aeb45fa28337ab434be9cb2874a35eab
https://hal.archives-ouvertes.fr/hal-03189229
https://hal.archives-ouvertes.fr/hal-03189229
Publikováno v:
SSRN Electronic Journal.
PRELIMINARY DRAFT We discuss maximum likelihood (ML) analysis for panel count data models, in which the observed counts are linked via a measurement density to a latent Gaussian process with spatial as well as temporal dynamics and random effects. Fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4dbca6b461b79054f570da6e1de1d60
https://doi.org/10.2139/ssrn.2616602
https://doi.org/10.2139/ssrn.2616602