In microarray analysis, people are interested in those features that have different characters in diseased samples compared to normal samples. The usual p-value method of selecting significant genes either gives too many false positives or cannot detect all the significant features. The False Discovery Rate (FDR) method controls false positives and at the same time selects significant features. We introduced Benjamini's method and Storey's method to control FDR, applied the two methods to human Meningioma data. We found that Benjamini's method is more conservative and that, after the number of the tests exceeds a threshold, increase in number of tests will lead to decrease in number of significant genes. In the second chapter, we investigate ways to search interesting gene expressions that cannot be detected by linear models as t-test or ANOVA. We propose a novel approach to use quadratic logistic regression to detect genes in Meningioma data that have non-linear relationship within phenotypes. By using quadratic logistic regression, we can find genes whose expression correlates to their phenotypes both linearly and quadratically. Whether these genes have clinical significant is a very interesting question, since these genes most likely be neglected by traditional linear approach.

After a full first term without any Supreme Court nominations, and almost no cooperation from the Court in regards to the New Deal, President Franklin D. Roosevelt decided to introduce a Court-packing plan into the Legislature that would allow him to add six new justices to the Court if necessary. The Bill failed in both the House and Senate but time allowed Roosevelt to have his chance to “pack” the court. Roosevelt nominated some of the best justices the Court has seen, and with their service the Supreme Court was forever changed.

Revised; Originally published: 2009 / 6 pp. / Proper irrigation management requires that growers assess their irrigation needs by taking measurements of various physical parameters. Some use sophisticated equipment while others use tried and true common sense approaches. Whichever method used, each has merits and limitations. In developing any irrigation management strategy, two questions are common: “When do I irrigate?” and “How much do I apply?” This bulletin deals with the WHEN.

The purpose of this work was first to describe the requirements for cryptographic protocols. Furthermore, the classification of these protocols should have been made with specific examples given. The aim of the next part of the work was to describe the methods which are suitable for description and modeling of cryptographic protocols. This work also addressed the analysis of cryptographic protocols by appropriate analytical means. The CSP method for modeling of the cryptographic protocols was applied in the practical part. The Yahalom protocol was selected as a protocol suitable for modeling. Two analysis was made. The first analysis concerned the standard version of the Yahalom protocol, which was tested to the requirements of cryptographic properties of the secrecy and authenticity. The second analysis was based on the possibility of disclosure of the key, including counterexamples and traces given by FDR. The first analysis did not reveal any weakening, in terms of two cryptographic properties. To demonstrate the possibility of FDR, Yahalom protocol was modified in order to cause the situation when the disclosure of keys appears. FDR then finds the exact procedure that an intruder must make to get the possession of the key.

No description available.

This dissertation is focused on multiple testing procedures to be used in data that are naturally grouped or possess a spatial structure. We propose `Two-Stage' procedure to control the False Discovery Rate (FDR) in situations where one-sided hypothesis testing is appropriate, such as astronomical source detection. Similarly, we propose a `Three-Stage' procedure to control the mixed directional False Discovery Rate (mdFDR) in situations where two-sided hypothesis testing is appropriate, such as vegetation monitoring in remote sensing NDVI data. The Two and Three-Stage procedures have provable FDR/mdFDR control under certain dependence situations. We also present the Adaptive versions which are examined under simulation studies. The `Stages' refer to testing hypotheses both group-wise and individually, which is motivated by the belief that the dependencies among the p-values associated with the spatially oriented hypotheses occur more locally than globally. Thus, these `Staged' procedures test hypotheses in groups that incorporate the local, unknown dependencies of neighboring p-values. If a group is found significant, further investigation is done to the individual p-values within that group. For the vegetation monitoring data, we extend the investigation by providing some spatio-temporal models and forecasts to some regions where significant change was detected through the multiple testing procedure. / Statistics

The technical advancements in genomics, functional magnetic-resonance and other areas of scientific research seen in the last two decades have led to a burst of interest in multiple testing procedures. A driving factor for innovations in the field of multiple testing has been the problem of large scale simultaneous testing. There, the goal is to uncover lower--dimensional signals from high--dimensional data. Mathematically speaking, this means that the dimension d is usually in the thousands while the sample size n is relatively small (max. 100 in general, often due to cost constraints) --- a characteristic commonly abbreviated as d >> n. In my thesis I look at several multiple testing problems and corresponding procedures from a false discovery rate (FDR) perspective, a methodology originally introduced in a seminal paper by Benjamini and Hochberg (2005). FDR analysis starts by fitting a two--component mixture model to the observed test statistics. This mixture consists of a null model density and an alternative component density from which the interesting cases are assumed to be drawn. In the thesis I proposed a new approach called log--FDR to the estimation of false discovery rates. Specifically, my new approach to truncated maximum likelihood estimation yields accurate null model estimates. This is complemented by constrained maximum likelihood estimation for the alternative density using log--concave density estimation. A recent competitor to the FDR is the method of \"Higher Criticism\". It has been strongly advocated in the context of variable selection in classification which is deeply linked to multiple comparisons. Hence, I also looked at variable selection in class prediction which can be viewed as a special signal identification problem. Both FDR methods and Higher Criticism can be highly useful for signal identification. This is discussed in the context of variable selection in linear discriminant analysis (LDA), a popular classification method. FDR methods are not only useful for multiple testing situations in the strict sense, they are also applicable to related problems. I looked at several kinds of applications of FDR in linear classification. I present and extend statistical techniques related to effect size estimation using false discovery rates and showed how to use these for variable selection. The resulting fdr--effect method proposed for effect size estimation is shown to work as well as competing approaches while being conceptually simple and computationally inexpensive. Additionally, I applied the fdr--effect method to variable selection by minimizing the misclassification rate and showed that it works very well and leads to compact and interpretable feature sets.

In der vorliegenden Habilitationsschrift werden im ersten Teil bekannte aber auch ausgewählte neue Verfahren zum Fügen von Metallen mit Kunststoffen vorgestellt und miteinander verglichen. Im zweiten Teil werden verschiedene Berechnungsmöglichkeiten für die Auslegung bzw. die Nachweisführung von mechanisch gefügten Verbindungen vorgestellt. Hierbei handelt es sich ebenfalls um bekannte Konzepte als auch um neue Konzepte aus eigenen Forschungsvorhaben. Anhand der Aufbereitung dieser Konzepte in Algorithmen nach DIN 66001 und der Bereitstellung von Beispielen, welche sich auf die Algorithmen beziehen, sind diese einfach anwendbar und für die studentische Ausbildung gut geeignet.:1 Bekannte und neue Fügeverfahren im Leichtbau 1.1 Einleitung zum ersten Kapitel 1.2 Clinchen 1.3 Ultrasonic Fusion Bonding and Clinching 1.4 Clinching with a lost Ring 1.5 Blindnieten 1.6 Flow Drilling Riveting 1.7 Zusammenfassung 2 Auslegungsmöglichkeiten und Bemessungskonzepte 2.1 Einleitung zum zweiten Kapitel 2.2 Berechnung von Vollnietverbindungen nach DIN EN 1993-1-8 2.3 Berechnung von Blindnietverbindungen nach J. Grandt 2.4 Berechnung von Clinch-, Stanzniet- und Blindnietverbindungen nach der Grenznahtfestigkeit / In the first part of this habilitation thesis, known but also selected new processes for joining metals with plastics presented and compared. In the second part, various calculation options for the design and verification of mechanically joined connections are presented. These are also well-known concepts as well as new concepts from our own research projects. Based on the processing of these concepts in algorithms according to DIN 66001 and the provision of examples that refer to the algorithms, they are easy to use and well suited for student training.:1 Bekannte und neue Fügeverfahren im Leichtbau 1.1 Einleitung zum ersten Kapitel 1.2 Clinchen 1.3 Ultrasonic Fusion Bonding and Clinching 1.4 Clinching with a lost Ring 1.5 Blindnieten 1.6 Flow Drilling Riveting 1.7 Zusammenfassung 2 Auslegungsmöglichkeiten und Bemessungskonzepte 2.1 Einleitung zum zweiten Kapitel 2.2 Berechnung von Vollnietverbindungen nach DIN EN 1993-1-8 2.3 Berechnung von Blindnietverbindungen nach J. Grandt 2.4 Berechnung von Clinch-, Stanzniet- und Blindnietverbindungen nach der Grenznahtfestigkeit

No description available.

Cette thèse est motivée par l’analyse des données issues de l’imagerie par résonance magnétique fonctionnelle (IRMf). La nécessité de développer des méthodes capables d’extraire la structure sous-jacente des données d’IRMf constitue un challenge mathématique attractif. A cet égard, nous modélisons les réseaux de connectivité cérébrale par un graphe et nous étudions des procédures permettant d’inférer ce graphe.Plus précisément, nous nous intéressons à l’inférence de la structure d’un modèle graphique non orienté par une procédure de test multiple. Nous considérons deux types de structure, à savoir celle induite par la corrélation et celle induite par la corrélation partielle entre les variables aléatoires. Les statistiques de tests basées sur ces deux dernières mesures sont connues pour présenter une forte dépendance et nous les supposerons être asymptotiquement gaussiennes. Dans ce contexte, nous analysons plusieurs procédures de test multiple permettant un contrôle des arêtes incluses à tort dans le graphe inféré.Dans un premier temps, nous questionnons théoriquement le contrôle du False Discovery Rate (FDR) de la procédure de Benjamini et Hochberg dans un cadre gaussien pour des statistiques de test non nécessairement positivement dépendantes. Nous interrogeons par suite le contrôle du FDR et du Family Wise Error Rate (FWER) dans un cadre gaussien asymptotique. Nous présentons plusieurs procédures de test multiple, adaptées aux tests de corrélations (resp. corrélations partielles), qui contrôlent asymptotiquement le FWER. Nous proposons de plus quelques pistes théoriques relatives au contrôle asymptotique du FDR.Dans un second temps, nous illustrons les propriétés des procédures contrôlant asymptotiquement le FWER à travers une étude sur simulation pour des tests basés sur la corrélation. Nous concluons finalement par l’extraction de réseaux de connectivité cérébrale sur données réelles. / This thesis is motivated by the analysis of the functional magnetic resonance imaging (fMRI). The need for methods to build such structures from fMRI data gives rise to exciting new challenges for mathematics. In this regards, the brain connectivity networks are modelized by a graph and we study some procedures that allow us to infer this graph.More precisely, we investigate the problem of the inference of the structure of an undirected graphical model by a multiple testing procedure. The structure induced by both the correlation and the partial correlation are considered. The statistical tests based on the latter are known to be highly dependent and we assume that they have an asymptotic Gaussian distribution. Within this framework, we study some multiple testing procedures that allow a control of false edges included in the inferred graph.First, we theoretically examine the False Discovery Rate (FDR) control of Benjamini and Hochberg’s procedure in Gaussian setting for non necessary positive dependent statistical tests. Then, we explore both the FDR and the Family Wise Error Rate (FWER) control in asymptotic Gaussian setting. We present some multiple testing procedures, well-suited for correlation (resp. partial correlation) tests, which provide an asymptotic control of the FWER. Furthermore, some first theoretical results regarding asymptotic FDR control are established.Second, the properties of the multiple testing procedures that asymptotically control the FWER are illustrated on a simulation study, for statistical tests based on correlation. We finally conclude with the extraction of cerebral connectivity networks on real data set.