Every time one counts the number of occurrences of a pair of values for two categorical variables, one obtains a contingency table. These tables are one of the simplest representations of data in order to statistically test for the presence of some association between the two variables under consideration. Although naturally occurring in so many scientific disciplines, there is still a lot of debate on the appropriate way to perform tests of significance on these contingency tables. Especially when one wants to use exact methods, i.e., methods that are based on the exact probabilities of observing the table of interest, there is great disagreement on which marginal totals one should treat as fixed for inference. This has led to the development of the conditional tests, most famously Fisher's exact test, and unconditional tests, of which Barnard's CSM test was the first example. Mostly due to philosophical objections and computational challenges, the unconditional test has received far less attention over the years. This is especially true for contingency tables with more than 2 rows or columns. To our knowledge, there are no implementations available of exact unconditional tests for these larger tables. The aim of this text is two-fold. First, we give a historical account on the rivalry between conditional and unconditional test, and argue that there is a case to be made to research exact unconditional methods in greater depth. Second, we will present implementations of exact unconditional tests that are applicable to general r×c contingency tables. Some of these implementations are generalisations of existing methods for the 2×2 table, such as Barnard's CSM test, with some additions in order to increase the computational efficiency. In addition, we also introduce a new approach that translates the classical Neyman-Pearson procedure of constructing a critical region for a given significance level α into a a mixed integer linear programming problem. The latter can be solved efficiently with one of many existing optimisation software packages. This will eventually lead to a power study comparing 14 different tests, of which 12 unconditional ones, for different table dimensions and marginal totals. Although no test comes out as most powerful in every situation, the tests using a linear programming formulation have comparable, and often higher power than the classical unconditional approaches. This comes at a cost however, the critical regions produced via this optimisation approach are not guaranteed to be nested, i.e., they are not necessarily contained in each other for increasing values of α. This limits their use and interpretability. Further research should point out whether additional requirements can be formulated that would make the critical regions nested, while still keeping the advantages of the linear programming formulation.

Statistics Netherlands performs many different surveys to obtain estimates of unknown characteristics of the Dutch population. To keep the response burden on the Dutch households low, Statistics Netherlands applies a screening procedure to their selected samples. In our research, we investigate the effects of the screening procedure on the survey sampling process. We conclude that the effects of the screening process cannot be considered negligible. We derive an approximation of the inclusion probability of an element in the sample after screening. This probability is dependent on the number of people on address and the sampling fraction. Consequently, the probability is not equal for all inhabitants and the effects of the screening procedure become larger as sample sizes increase. Two different statistical tests are developed and applied to existing samples that have recently been selected and screened by Statistics Netherlands, to determine whether the sample after screening is representative for the population (and for the sample before screening) with respect to relevant auxiliary variables. From a super-population viewpoint, we investigate the properties of the generalised regression estimator. We prove that under modest conditions the generalised regression estimator is consistent and asymptotically unbiased for the self-weighting two-stage sampling design that is used at Statistics Netherlands. When screening is applied, we cannot conclude that the generalised regression estimator is consistent and asymptotically unbiased. We show how the Horvitz-Thompson estimator and the generalised regression estimator can be used to undo the effects of the screening procedure during the estimation of population characteristics.

This thesis investigates the robustness of multivariate S-estimators, which are statistical methods used to estimate the location and covariance parameters of multivariate distributions. Outliers, or atypical observations, can significantly impact statistical analyses, leading to incorrect conclusions. Robust methods, such as S-estimators, aim to reduce the influence of outliers, providing more reliable analysis results. The primary objective is to assess the effectiveness of S-estimators through simulations using the statistical package R.

When a second tumor arises in the contralateral breast in a patient with a previous or synchronous breast cancer, it is of clinical importance to determine if this tumor is a new unrelated tumor or a metastasis, i.e. clone, of the primary tumor. A new, unrelated tumor may be treated similarly as the first one since treatment was successful, while a distant metastasis demands a change of therapy and has a more adverse prognosis. In clinic, a second tumor is generally regarded as a new primary. If there is clinical suspicion that the second tumor may be a metastasis, clinico-pathological characteristics of the two tumors are used assess the clonality status. Clinico-pathological characteristics, however, are not reliable predictors to determine if a second tumor is a metastasis. Recent studies have investigated tumor clonality using techniques from molecular genetics. These models appear to perform well, but have several drawbacks. In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values. The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available. The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs.

This thesis is on the subject of modelling the probability of default in a low default portfolio. In these portfolios there is a high risk of underestimating the true probability of default. Two models are considered, a Gaussian one factor model and a Poisson model with Gamma mixture. Classical estimation methods as the maximum likelihood are shown to fail to produce conservative results, and therefore the Bayesian approach is considered. New on the subject is the consideration of multivariate prior distributions, which are shown to be an improvement of the univariate case.

In this thesis we address the problem of estimating a curve of interest (which might be a probability density, a failure rate or a regression function) under monotonicity constraints. The main concern is investigating large sample distributional properties of smooth isotonic estimators, which have a faster rate of convergence and a nicer graphical representation compared to standard isotonic estimators such as the constrained nonparametric maximum likelihood and the Grenander-type estimator. In the first part, we focus on the pointwise behavior of estimators for the hazard rate in the right censoring and Cox regression models, while the second part is dedicated to global errors of estimators in a general setup, which includes estimation of a probability density, a failure rate, or a regression function. We provide central limit theorems and assess the finite sample performance of the estimators by means of simulation studies for constructing confidence intervals and goodness of fit tests. @en