在處理列聯表時，適合度檢定的結果如果是顯著的話，則意味著配適的模式並不恰當，這其中一個可能的原因是資料中存在離群細格．因此我們希望能夠針對問題癥結所在，找出離群細格，使得我們的資料可以利用一個比較簡單且容易解釋的模式來做分析．在這篇論文中，我們主要依據施苑玉[1995]所提出的方法作些許的改變，使得改進後的方法可以適用於三維列聯表的所有情形．此外我們也將 Simonoff 在1988年所提出的方法，以及 BMDP 統計軟體的程序 4F ，與我們所提出的方法相比較．由模擬實驗的結果可發現我們的方法比前述兩種方法更具可行性． / When fitting a loglinear model to a contingency table, a significant goodness-of-fit can be resulted because of the existence of a few outlyingcells. Since a simpler model is easier to interpret and conveys more easilyunderstood information about a table than a complicated one, we would liketo identify those outliers so that a simpler model would fit a given data set. In this research, a modification of Shih's [1995] procedure is provided, and the revised method is now applicable to any type of models related tothree-way tables. Some data sets are simulated to compare outliers detectedusing procedures proposed by Simonoff [1988], and BMDP program 4F with our proposed method. Based on the results through simulation, our revised procedure outperforms the other two procedures most of the time.

在處理列聯表(Contingency table)資料時，一般我們常用卡方適合度檢定(chi-squared goodess-of-fit test)來判定模式配適的好壞。如果這個檢定是顯著的，則意謂著配適的模式並不恰當，我們則希望進一步探討可能的原因何在。這其中的一個可能原因是資料中存在所謂的離群細格(outlying cell)，這些細格的觀測次數和其他細格的觀測次數呈現某種不一致的現象。 　　在以往的文獻中，離群細格的偵測，通常藉由不同定義的殘差(residual)作為工具，進而衍生出各種不同的偵測方法。只是，這些探討基本上僅局限於二維列聯表的情形，對於高維度的列聯表，並沒有作更進一步的詮釋。Brown (1974)提出一個逐步偵測的方法，可依序找出所有可能的離群細格，直到近似獨立(quasi-independence)的模式假設不再顯著為止。但是我們認為他所引介的這個方法所牽涉的計算程序似乎過於繁複，因此藉由簡化修改計算過程，我們提供了另一種離群細格偵測的方法。依據模擬實驗的結果發現，本文所介紹的方法與Brown的方法作比較只有過之而無不及。此外我們也探討了應用此種方法到三維列聯表的可行性和可能遭遇到的困難。 / Chi-squared goodness-of-fit tests are usually employed to test whether a model fits a contingency table well. When the test is significant, we would then like to identify the sources that cause significance. The existence of outlying cells that contribute heavily to the test statistic may be one of the reasons. 　　Brown (1974) offered a stepwise criteria for detecting outlying cells in two-way con-tingency tables. In attempt to simplify the lengthy calculations that are required in Brown's method, we suggest an alternative procedure in this study. Based on simulation results, we find that the procedure performs reasonably well, it even outperforms Brown's method on several occasions. In addition, some extensions and issues regarding three-way contingency tables are also addressed.

Dans cette thèse, nous étudions trois modèles de percolation planaire : la percolation de Bernoulli, la percolation de Voronoi, et la percolation de lignes nodales. La percolation de Bernoulli est souvent considérée comme le modèle le plus simple à définir admettant une transition de phase. La percolation de Voronoi est quant à elle un modèle de percolation de Bernoulli en environnement aléatoire. La percolation de lignes nodales est un modèle de percolation de lignes de niveaux de champs gaussiens lisses. Deux fils conducteurs principaux ont guidé nos travaux. Le premier est la recherche de similarités entre ces modèles, en ayant à l'esprit que l'on s'attend à ce qu'ils admettent tous la même limite d'échelle. Nous montrons par exemple que le niveau critique de la percolation de lignes nodales est égal au niveau auto-dual (à savoir le niveau zéro) lorsque le champ considéré est le champ de Bargmann-Fock, qui est un champ gaussien analytique naturel. Le deuxième fil conducteur est l'étude de dynamiques sur ces modèles. Nous montrons en particulier que, si on considère un modèle de percolation de Voronoi critique et si on laisse les points se déplacer selon des processus de Lévy stables à très longue portée, alors il existe des temps exceptionnels avec une composante non bornée / We study three models of percolation in the plane: Bernoulli percolation, Voronoi percolation, and nodal lines percolation. Bernoulli percolation is often considered as the simplest model which admits a phase transition. Voronoi percolation is a Bernoulli percolation model in random environment. Nodal lines percolation is a level lines percolation model for smooth planar Gaussian fields. We have followed two main threads. The first one is the resarch of similarities between these models, having in mind that we expect that they admit the same scaling limit. We show for instance that the critical level for nodal lines percolation is the self-dual level (namely the zero level) if the Gaussian field is the Bargmann-Fock field, which is natural analytical field. The second main thread is the study of dynamics on these percolation models. We show in particular that if we sample a critical Voronoi percolation model and if we let each point move according to a long range stable Lévy process, then there exist exceptional times with an unbounded cluster