. . "Linda, Bohdan" . "63" . "15"^^ . . . . . "25410" . "2"^^ . . . "Modelling of the development of the selected indicators of the insurance market"@en . "2"^^ . . "[236E6CD2F576]" . "RIV/00216275:25410/11:39894562" . "RIV/00216275:25410/11:39894562!RIV13-GA0-25410___" . "Modelling of the development of the selected indicators of the insurance market"@en . . . "Modelling of the development of the selected indicators of the insurance market" . "The main limitation of many statistical methods consists in the fact that they usually require fulfilments of many different assumptions that cannot often be verified. This problem arises frequently at the time series, too. In this case it is helpful to use bootstrap methods. This paper deals with the application of the bias reduced bootstrap method to the Box-Jenkins methodology. Several methods of nonparametric bootstrap for a bias reduced estimate of the autoregressive parameters ? of AR(1) and AR(2) are presented in this paper: model oriented bootstrap, overlapping moving blocks and not-overlapping moving blocks. A comparison of the results of the classical and resampling methods is performed for the premium written time series." . . "Insurance, financial covering of the risk, parameters estimate, bootstrap method, autoregressive models, overlapping moving blocks, not overlapping moving blocks, extrapolation."@en . "Kubanov\u00E1, Jana" . "Modelling of the development of the selected indicators of the insurance market" . . "P(GA402/09/1866)" . "IN - Indick\u00E1 republika" . "0008-0683" . "213117" . "Calcutta Statistical Association bulletin" . . . . . . "249-252" . . . "The main limitation of many statistical methods consists in the fact that they usually require fulfilments of many different assumptions that cannot often be verified. This problem arises frequently at the time series, too. In this case it is helpful to use bootstrap methods. This paper deals with the application of the bias reduced bootstrap method to the Box-Jenkins methodology. Several methods of nonparametric bootstrap for a bias reduced estimate of the autoregressive parameters ? of AR(1) and AR(2) are presented in this paper: model oriented bootstrap, overlapping moving blocks and not-overlapping moving blocks. A comparison of the results of the classical and resampling methods is performed for the premium written time series."@en . . .