공간자료 해석을 위한 벌칙우도 방법론에 대한 검토

A review of penalized likelihood method for spatial data analysis

In this research, we review the penalized likelihood method, in the view of applying the method to spatial data analysis. We considered a few groups of specific topics. One is the topics of determining the weighting factor of penalty term. Determining the weighting factor is equivalent to determining the degree of freedom of the selected statistical model. In spatial statistics, infill asymptotic setting is a more reasonable framework than increasing domain asymptotic setting, but not a few research have considered the infill asymptotic setting of the weighting factor. We review the meaning of the topic. The other topic is to review the effects of method using data-adjusted bases. Differently with Kriging in spatial statistics and well known methods in time series models using data-adjusted bases for prediction, reproducing kernel Hilbert space method and fast Fourier transform method adopt the bases obtained independently from data. We considered the necessity of the comparative researches between the methods using data-adjusted bases and simple mathematical bases.