| Summary: | 碩士 === 國立彰化師範大學 === 統計資訊研究所 === 105 === Variable selection is an active research topic and has attracted much attention in many scientific fields. However, if a variable selection process is unstable, inferences based on the selected model may be unreliable. To avoid the influence of selection instability, model averaging becomes a popular technique but has not received much attention especially for spatial regression models. In this thesis, we focus on discussing spatial regression model averaging based on conditional information criteria. We consider relatively few models for averaging, where a novel idea based on the values of information criteria is proposed to determine model weights. It results in the spatial predictor that is comparable to the conventional model averaging approaches and is computationally more efficient. Statistical inferences of the proposed methodology are justified both theoretically and numerically. Finally, an application of a mercury data set for lakes in Maine is analyzed for illustration.
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