Fixed effect estimation and spatial prediction via universal kriging

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王偲穎(Si-Ying Wang) 查詢紙本館藏

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統計研究所

論文名稱

(Fixed effect estimation and spatial prediction via universal kriging)

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摘要(中)

空間統計廣泛應用於地質、大氣、水文、生態等相關領域的資料分析。而研究區域的空間預測和解釋變數的選擇都是空間統計學中的重要研究議題。為了瞭解固定效應項，研究者需要收集足夠的解釋變數並選擇適當的解釋變數來做後續的建模與空間預測。在實際資料分析中，解釋變數收集不易且常有收集不完全的情況，且收集多個解釋變數通常需花費大量的人力與成本。本篇論文使用一個以抽樣位置所決定的基底函數集合來取代解釋變數的角色，省去收集解釋變數的人力與成本，同時也可以估計固定效應項的趨勢，進而得到準確的空間預測曲面。從模擬實驗結果可知，我們所提的方法有不錯的預測表現；最後，本篇論文也應用所提的方法去分析孟加拉地下水的數據，並得到砷汙染在孟加拉地區的汙染濃度預測曲面，說明我們所提方法的實用性與有效性。

摘要(英)

Spatial statistics is widely used in geology, atmosphere, hydrology, ecology and other related fields. In spatial statistics, spatial prediction and selection of appropriate covariates both are important issues. To understand the fixed effect clearly, researchers generally need to collect enough covariates and select a suitable subset of covariates based on some selection criteria. Then, the corresponding spatial predicted surface can be obtained. In practice, collecting covariates is difficult and it is often incomplete. In this thesis, a class of basis functions only determined by the sampling locations is applied to replace the possible covariates. In other words, we do not need any covariates and it saves the manpower and the cost of collecting covariates. Combining a selection criterion to select the number of basis functions, the trend of fixed effect can be estimated and the consequent spatial predicted surface also can be obtained. From simulation studies, we can see that our proposed method has better performance than the conventional methods under various situations. Finally, we illustrate the utility of the proposed method by a real data application concerning the groundwater data in Bangladesh.

關鍵字(中)

★ 基底函數
★ 選取準則
★ 平滑樣條
★ 空間預測
★ 變數選取

關鍵字(英)

★ Basis functions
★ selection criterion
★ smoothing spline
★ spatial prediction
★ variable selection

論文目次

摘要 i
Abstract ii
致謝辭 iii
Contents iv
Figure contents v
Table contents vi
1. Introduction 1
2. Spatial regression model, Parameter estimation and spatial prediction 3
2.1 Spatial regression model 3
2.2 Parameter estimation and spatial prediction 5
3. Our method 7
3.1 Basis functions 7
3.2 Model selection criteria 11
3.3 Spatial prediction after model selection 16
4. Simulation study 17
4.1 Simulation scenarios 17
4.2 Simulation results 20
5. Data analysis 27
6. Conclusion and discussion 33
References 35

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指導教授

陳春樹(Chun-Shu Chen)

審核日期

2021-6-29

推文