Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Ruli, Erlis"'
Autor:
Bonato, Matteo, Baronchelli, Ivano, Casasola, Viviana, De Zotti, Gianfranco, Trobbiani, Leonardo, Ruli, Erlis, Tailor, Vidhi, Bianchi, Simone
We exploit the DustPedia sample of galaxies within approximately 40 Mpc, selecting 388 sources, to investigate the correlations between IR luminosity (L$_{\rm IR}$), the star formation rate (SFR), and the CO(1-0) luminosity (L$_{\rm CO}$) down to muc
Externí odkaz:
http://arxiv.org/abs/2407.10801
A confidence distribution is a distribution for a parameter of interest based on a parametric statistical model. As such, it serves the same purpose for frequentist statisticians as a posterior distribution for Bayesians, since it allows to reach poi
Externí odkaz:
http://arxiv.org/abs/2109.01219
Autor:
Girardi, Paolo, Greco, Luca, Mameli, Valentina, Musio, Monica, Racugno, Walter, Ruli, Erlis, Ventura, Laura
We discuss an approach for fitting robust nonlinear regression models, which can be employed to model and predict the contagion dynamics of the Covid-19 in Italy. The focus is on the analysis of epidemic data using robust dose-response curves, but th
Externí odkaz:
http://arxiv.org/abs/2004.03187
Autor:
Ruli, Erlis
Recently, many authors have cast doubts on the validity of ABC model choice. It has been shown that the use of sufficient statistic in ABC model selection leads, apart from few exceptional cases in which the sufficient statistic is also cross-model s
Externí odkaz:
http://arxiv.org/abs/1803.10749
Publikováno v:
Test 2019
Standard Bayesian analyses can be difficult to perform when the full likelihood, and consequently the full posterior distribution, is too complex and difficult to specify or if robustness with respect to data or to model misspecifications is required
Externí odkaz:
http://arxiv.org/abs/1711.10819
We discuss an approach for deriving robust posterior distributions from $M$-estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use $M$-estimating functions to construct suitable summary statistics in ABC algo
Externí odkaz:
http://arxiv.org/abs/1706.01752
Publikováno v:
Bayesian Analysis 2015, Vol. 10, No. 2, 411-439
Approximate Bayesian Computation (ABC) is a useful class of methods for Bayesian inference when the likelihood function is computationally intractable. In practice, the basic ABC algorithm may be inefficient in the presence of discrepancy between pri
Externí odkaz:
http://arxiv.org/abs/1505.03350
Publikováno v:
Electronic Journal of Statistics 10(2), 3986-4009, 2016
Statistical applications often involve the calculation of intractable multidimensional integrals. The Laplace formula is widely used to approximate such integrals. However, in high-dimensional or small sample size problems, the shape of the integrand
Externí odkaz:
http://arxiv.org/abs/1502.06440
Publikováno v:
In Journal of Statistical Planning and Inference March 2020 205:10-22
Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intractable. We propose to use composite likelihood score functions as summa
Externí odkaz:
http://arxiv.org/abs/1311.7286