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A spatial autoregressive process is investigated, where the autoregressive coefficients are equal, and their sum is one. It is shown that the limiting distribution of the least squares estimator for this coefficient is normal and, in contrast to the doubly geometric process, the rate of convergence is n−5/4.

- Sándor Baran, Gyula Pap
- J. Multivariate Analysis
- 2012

- Sándor Baran
- 2016

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… (More)

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with… (More)

- Sándor Baran, Kinga Sikolya, Lajos Veress
- Communications in Statistics - Simulation and…
- 2013

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views… (More)

Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post-processing. Univariate post-processing models such as Bayesian model averaging (BMA) have been successfully applied for… (More)

- Sándor Baran
- Computational Statistics & Data Analysis
- 2014

Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather predictionmodels, in order to create calibrated predictive probability density functions (PDFs). The BMApredictive PDF of the futureweather quantity is themixture of the individual PDFs… (More)

- Sándor Baran, Gyula Pap
- J. Multivariate Analysis
- 2009

A nearly unstable sequence of stationary spatial autoregressive processes is investigated, when the sum of the absolute values of the autoregressive coefficients tends to one. It is shown that after an appropriate norming the least squares estimator for these coefficients has a normal limit distribution. If none of the parameters equals zero than the… (More)

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