Válogatás az elmúlt év publikációiból
Baran, Á, Noszály, Cs., Vertse, T., JOZSO, a computer code for calculating broad neutron resonances. Comp. Phys. Com. 228 (2018), 185--191. Impakt faktor: 3.748, SJR: D1
A renewed version of the computer code GAMOW (Vertse et al., 1982) is given in which the difficulties in calculating broad neutron resonances are amended. New types of phenomenological neutron potentials with strict finite range are built in. Landscape of the S-matrix can be generated on a given domain of the complex wave number plane and S-matrix poles in the domain are localized. Normalized Gamow wave functions and trajectories of given poles can be calculated optionally.
Baran, S., Lerch, S., Combining predictive distributions for statistical post-processing of ensemble forecasts. International Journal of Forecasting 34 (2018), 477-496.(IF: 2.186; SJR: D1) http://dx.doi.org/10.1016/j.ijforecast.2018.01.005
Statistical post-processing techniques are now used widely for correcting systematic biases and errors in the calibration of ensemble forecasts obtained from multiple runs of numerical weather prediction models. A standard approach is the ensemble model output statistics (EMOS) method, which results in a predictive distribution that is given by a single parametric law, with parameters that depend on the ensemble members. This article assesses the merits of combining multiple EMOS models based on different parametric families. In four case studies with wind speed and precipitation forecasts from two ensemble prediction systems, we investigate the performances of state of the art forecast combination methods and propose a computationally efficient approach for determining linear pool combination weights. We study the performance of forecast combination compared to that of the theoretically superior but cumbersome estimation of a full mixture model, and assess which degree of flexibility of the forecast combination approach yields the best practical results for post-processing applications.
Lerch, S., Baran, S., Similarity-based semi-local estimation of EMOS models. Journal of the Royal Statistical Society. Series C: Applied Statistics 66 (2017), no. 1, 29-51. (IF: 1.750; SJR: Q1) https://doi.org/10.1111/rssc.12153
Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical post‐processing. In the ensemble model output statistics approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members. The paper proposes two semilocal methods for estimating the ensemble model output statistics coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined by using either distance functions or clustering based on various features of the climatology, forecast errors and locations of the observation stations. In a case‐study on wind speed over Europe with forecasts from the ‘Grand limited area model ensemble prediction system’, the similarity‐based semilocal models proposed show significant improvement in predictive performance compared with standard regional and local estimation methods. They further allow for estimating complex models without numerical stability issues and are computationally more efficient than local parameter estimation.
Baran, S., K-optimal designs for parameters of shifted Ornstein-Uhlenbeck processes and sheets. Journal of Statistical Planning and Inference 186 (2017), 28-41. (IF: 0.814; SJR Q1-Q2) https://doi.org/10.1016/j.jspi.2017.02.003
Continuous random processes and fields are regularly applied to model temporal or spatial phenomena in many different fields of science, and model fitting is usually done with the help of data obtained by observing the given process at various time points or spatial locations. In these practical applications sampling designs which are optimal in some sense are of great importance. We investigate the properties of the recently introduced K-optimal design for temporal and spatial linear regression models driven by Ornstein–Uhlenbeck processes and sheets, respectively, and highlight the differences compared with the classical D-optimal sampling. A simulation study displays the superiority of the K-optimal design for large parameter values of the driving random process.
Fazekas I., Porvázsnyik B., Limit theorems for the weights and the degrees in an N-interactions random graph model. Open Mathematics, 14(1) pp. 414-424 (2016), DOI: 10.1515/math-2016-0039
A random graph evolution based on interactions of N vertices is studied. During the evolution both the preferential attachment rule and the uniform choice of vertices are allowed. The weight of an M-clique means the number of its interactions. The asymptotic behaviour of the weight of a fixed M-clique is studied. Asymptotic theorems for the weight and the degree of a fixed vertex are also presented. Moreover, the limits of the maximal weight and the maximal degree are described. The proofs are based on martingale methods.
Burai, Pál(H-LAJO-NDM), Convexity with respect to families of sections and lines and their application in optimization. J. Global Optim. 64 (2016), no. 4, 649–662.
The main goal of this paper is to introduce two generalized convexity notions, to examine their properties and their use in optimization theory, in particular, to deduce necessary and sufficient first order conditions.
Baran, S., Probabilistic wind speed forecasting with truncated normal components. Computational Statistics and Data Analysis 75 (2014), 227-238. (IF: 1.400), http://dx.doi.org/10.1016/j.csda.2014.02.013
Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to create calibrated predictive probability density functions (PDFs). The BMA predictive PDF of the future weather quantity is the mixture of the individual PDFs corresponding to the ensemble members and the weights and model parameters are estimated using forecast ensembles and validating observations from a given training period. A BMA model for calibrating wind speed forecasts is introduced using truncated normal distributions as conditional PDFs and the method is applied to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and to the University of Washington Mesoscale Ensemble. Three parameter estimation methods are proposed and each of the corresponding models outperforms the traditional gamma BMA model both in calibration and in accuracy of predictions.
Baran, S., Lerch, S., Log-normal distribution based EMOS models for probabilistic wind speed forecasting. Quarterly Journal of the Royal Meteorological Society 141 (2015), 2289-2299. (IF: 3.669) http://onlinelibrary.wiley.com/doi/10.1002/qj.2521/abstract
Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles: they are usually underdispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind-speed forecasts based on the log-normal (LN) distribution and we also show a regime-switching extension of the model, which combines the previously studied truncated normal (TN) distribution with the LN. Both models are applied to wind-speed forecasts of the eight-member University of Washington mesoscale ensemble, the 50 member European Centre for MediumRange Weather Forecasts (ECMWF) ensemble and the 11 member Aire Limitee´ Adaptation dynamique Developpement International-Hungary Ensemble Prediction ´ System (ALADIN-HUNEPS) ensemble of the Hungarian Meteorological Service; their predictive performance is compared with that of the TN and general extreme value (GEV) distribution based EMOS methods and the TN–GEV mixture model. The results indicate improved calibration of probabilistic forecasts and accuracy of point forecastsin comparison with the raw ensemble and climatological forecasts. Further, the TN–LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with models utilizing the GEV distribution without assigning mass to negative values.
P. Salamon, Á. Baran, T. Vertse: Distributions of the S-matrix poles in Woods-Saxon and cut-off Woods-Saxon potentials Nuclear Physics A, 952 (2016) 1-17. http://dx.doi.org/10.1016/j.nuclphysa.2016.04.010
The positions of the l=0 S-matrix poles are calculated in generalized Woods–Saxon (GWS) potential and in cut-off generalized Woods–Saxon (CGWS) potential. The solutions of the radial equations are calculated numerically for the CGWS potential and analytically for GWS using the formalism of Gy. Bencze . We calculate CGWS and GWS cases at small non-zero values of the diffuseness in order to approach the square well potential and to be able to separate effects of the radius parameter and the cut-off radius parameter. In the case of the GWS potential the wave functions are reflected at the nuclear radius therefore the distances of the resonant poles depend on the radius parameter of the potential. In CGWS potential the wave function can be reflected at larger distance where the potential is cut to zero and the derivative of the potential does not exist. The positions of most of the resonant poles do depend strongly on the cut-off radius of the potential, which is an unphysical parameter. Only the positions of the few narrow resonances in potentials with barrier are not sensitive to the cut-off distance. For the broad resonances the effect of the cut-off cannot be corrected by using a suggested analytical form of the first order perturbation correction.
G. Stoyan, A. Baran, Elementary Numerical Mathematics for Programmers and Engineers Birkhäuser, 2016 (http://www.springer.com/in/book/9783319446592)
Mátyás Barczy, Kristóf Körmendi, Gyula Pap: Statistical inference for 2-type doubly symmetric critical irreducible continuous state and continuous time branching processes with immigration. Journal of Multivariate Analysis 139 (2015) 92--123.
Abstract: We study asymptotic behavior of conditional least squares estimators for 2-type doubly symmetric critical irreducible continuous state and continuous time branching processes with immigration based on discrete time (low frequency) observations.
Mátyás Barczy, Márton Ispány, Gyula Pap: Asymptotic behavior of conditional least squares estimators for unstable integer-valued autoregressive models of order 2. Scandinavian Journal of Statistics 41 (2014) 866--892.
Abstract: In this paper, the asymptotic behavior of the conditional least squares estimators of the autoregressive parameters, of the mean of the innovations, and of the stability parameter for unstable integer-valued autoregressive processes of order 2 is described. The limit distributions and the scaling factors are different according to the following three cases: (i) decomposable, (ii) indecomposable but not positively regular, and (iii) positively regular models.