Statistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results. / text

Master's thesis is focused on solution of the statistical problem to find a probability distribution of a discrete random variable on the basis of the observed data. These estimates are obtained by minimizing pseudo-quasinorm which is introduced here.The thesis further focuses on atributes of this pseudo-quasinorm. It also contains practical application of these methods.

O presente trabalho teve por objetivos propor um método para testar a contribuição de cada genótipo e ambiente para a interação genótipos X ambientes em ensaios multi-ambientais através de um teste F e implementar uma rotina computacional para a realização da análise de dados segundo o teste proposto. O estudo avalia quatro conjuntos de dados, cada um com diferentes números de genótipos dentro de ambientes com quatro blocos. Para um dos conjuntos, simulou-se as somas de quadrados das linhas (genótipos) e colunas (ambientes) da matriz de interação genótipos X ambientes (GE) gerando 500, 5000 e 10000 experimentos para verificar a distribuição empírica. Os resultados indicaram um ajuste à distribuição qui-quadrado não-central para as linhas e colunas da matriz de interação GE, verificados também pelo teste de Kolmogorov-Smirnov e o gráfico QQplot. Na aplicação do teste F proposto aos quatro conjuntos de dados, identificou-se os genótipos e ambientes que contribuiram mais para a interação genótipos X ambientes. Dessa forma, os melhoristas podem selecionar bons genótipos e ambientes nos seus estudos. / The objective of the present work was to propose a method for testing the con- tribution of each element in a genotypes X environments interaction using multi-environment analyses by means of an F test and implementation of a computational routine to analyze the data according to the test proposed. The study evaluated four data sets, each with a di®erent number of genotypes and environments, in a block design with four repetitions. In one group, the sum of squares within rows (genotypes) and columns (environments) of the genotypes X environments (GE) matrix was simulated, generating 500, 5000 and 10000 experiments to verify the empirical distribution. Results indicate a non-central chi-squared distribution for rows and columns of the GE interaction matrix, which was also verified by the Kolmogorov-Smirnov test and QQplot graph. Application of the F test to the four data sets identified the genotypes and environments that contributed the most to the genotypes X environments interaction. In this way, geneticists can select good genotypes and environments in their studies.

O presente trabalho teve por objetivos propor um método para testar a contribuição de cada genótipo e ambiente para a interação genótipos X ambientes em ensaios multi-ambientais através de um teste F e implementar uma rotina computacional para a realização da análise de dados segundo o teste proposto. O estudo avalia quatro conjuntos de dados, cada um com diferentes números de genótipos dentro de ambientes com quatro blocos. Para um dos conjuntos, simulou-se as somas de quadrados das linhas (genótipos) e colunas (ambientes) da matriz de interação genótipos X ambientes (GE) gerando 500, 5000 e 10000 experimentos para verificar a distribuição empírica. Os resultados indicaram um ajuste à distribuição qui-quadrado não-central para as linhas e colunas da matriz de interação GE, verificados também pelo teste de Kolmogorov-Smirnov e o gráfico QQplot. Na aplicação do teste F proposto aos quatro conjuntos de dados, identificou-se os genótipos e ambientes que contribuiram mais para a interação genótipos X ambientes. Dessa forma, os melhoristas podem selecionar bons genótipos e ambientes nos seus estudos. / The objective of the present work was to propose a method for testing the con- tribution of each element in a genotypes X environments interaction using multi-environment analyses by means of an F test and implementation of a computational routine to analyze the data according to the test proposed. The study evaluated four data sets, each with a di®erent number of genotypes and environments, in a block design with four repetitions. In one group, the sum of squares within rows (genotypes) and columns (environments) of the genotypes X environments (GE) matrix was simulated, generating 500, 5000 and 10000 experiments to verify the empirical distribution. Results indicate a non-central chi-squared distribution for rows and columns of the GE interaction matrix, which was also verified by the Kolmogorov-Smirnov test and QQplot graph. Application of the F test to the four data sets identified the genotypes and environments that contributed the most to the genotypes X environments interaction. In this way, geneticists can select good genotypes and environments in their studies.

IntroStat was designed to meet the needs of students, primarily those in business, commerce and management, for a course in applied statistics. IntroSTAT is designed as a lecture-book. One of the aims is to maximize the time spent in explaining concepts and doing examples. The book is commonly used as part of first year courses into Statistics.

Les océans subissent des pressions d'origine anthropique particulièrement fortes comme la surpêche, la pollution physico-chimique, et le bruit rayonné par les activités industrielles et militaires. Cette thèse se place dans un contexte de compréhension de l'impact du bruit rayonné dans les océans sur les mammifères marins. L'acoustique passive joue donc un rôle fondamental dans ce problème. Ce travail aborde la tâche de détection de signatures acoustiques de mammifères marins dans le spectrogramme. Cette tâche est difficile pour deux raisons : 1. le bruit océanique a une structure complexe (non-stationnaire, coloré), 2. les signaux de mammifères marins sont inconnus et possèdent eux aussi une structure complexe (non-stationnaires bande étroite et/ou impulsionnels). Le problème doit donc être résolu de manière locale en temps-fréquence, et ne pas faire d'hypothèse a priori sur le signal. Des détecteurs statistiques basés uniquement sur la connaissance des statistiques du bruit dans le spectrogramme existent, mais souffrent deux lacunes : 1. leurs performances en terme de probabilité de fausse alarme/ probabilité de détection se dégradent fortement à faible rapport signal à bruit, et 2. ils ne sont pas capables de séparer les signaux à bande étroite des signaux impulsionnels. Ce travail apporte des pistes de réflexion sur ces problèmes.L'originalité de ce travail de thèse repose dans la formulation d'un test d'hypothèse binaire prenant explicitement en compte l'organisation spatiale des pics temps-fréquence. Nous introduisons une méthode d'Analyse de la Densité des Fausses Alarmes (FADA) qui permet de discriminer les régions temps-fréquence abritant le signal de celles n'abritant que du bruit. Plus précisément,le nombre de fausses alarmes dans une région du plan est d'abord modélisé par une loi binomiale, puis par une loi binomiale corrélée, afin de prendre en considération la redondance du spectrogramme. Le test d'hypothèse binaire est résolu par une approche de Neyman-Pearson. Nous démontrons numériquement la pertinence de cette approche et nous la validons sur données réelles de mammifères marins disposant d'une grande variété de signaux et de conditions de bruit. En particulier, nous illustrons la capacité de FADA à discriminer efficacement le signal du bruit en milieu fortement impulsionnel. / The oceans experience heavy anthropogenic pressure due to overfishing, physico-chemical pollution, and noise radiated by industrial and military activities. This work focuses on the use of passive acoustic monitoring of the oceans, as a tool to understand the impact of radiated noise on marine ecosystems, and particularly on marine mammals. This work tackles the task of detection of acoustical signals of marine mammals using the spectrogram. This task is uneasy for two reasons : 1. the ocean noise structure is complex (non-stationary and colored) and 2. the signals of interest are unknown and also shows a complex structure (non-stationary narrow band and/or impulsive). The problem therefore must be solved locally without making a priori hypothesis on the signal. Statistical detectors only based on the local analysis of the noise spectrogram coefficients are available, making them suitable for this problem. However, these detectors suffer two disadvantages : 1. the trade-offs false alarm probability/ detection probability that are available for low signal tonoise ratio are not satisfactory and 2. the separation between narrow-band and impulsive signals is not possible. This work brings some answers to these problems.The main contribution of this work is to formulate a binary hypothesis test taking explicitly in account the spatial organization of time-frequency peaks. We introduce the False Alarm Density Analysis (FADA) framework that efficiently discriminates time-frequency regions hosting signal from the ones hosting noise only. In particular the number of false alarms in regions of the binary spectrogram is first modeled by a binomial distribution, and then by a correlated binomial distribution to take in account the spectrogram redundancy. The binary hypothesis test is solved using a Neyman-Pearson criterion.We demonstrate the relevance of this approach on simulated data and validate the FADA detector on a wide variety of real signals. In particular we show the capability of the proposed method to efficiently detect signals in highly impulsive environment.